Birth Weight Percentile Calculator Canada

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Birth Weight Percentile Calculator Canada

Understand your baby's growth relative to Canadian averages.

Birth Weight Percentile Calculator

Enter weight in kilograms (kg).
Enter age in completed weeks (e.g., 40 for 40 weeks).
Male Female Select the baby's sex.

Your Baby's Birth Weight Results

Average Weight: — kg
Standard Deviation: — kg
Z-Score:
The percentile is calculated using the baby's Z-score, which measures how many standard deviations their weight is from the mean for their gestational age and sex, based on Canadian reference data. A higher Z-score indicates a heavier baby for their age.

Weight Distribution Chart

Actual Baby Weight Average Weight

Canadian Birth Weight Data (Example)

Gestational Age (Weeks) Sex Mean Weight (kg) Standard Deviation (kg)
Sample data points for reference. Actual calculations use a more comprehensive dataset.

{primary_keyword}

Understanding your baby's birth weight is a significant milestone for new parents. In Canada, the {primary_keyword} is a crucial metric used by healthcare professionals to assess a newborn's size in relation to other babies born at the same gestational age and of the same sex. It's not just about a number; it's a vital indicator of fetal growth and can help identify potential health concerns early on. This percentile helps gauge whether a baby is small for gestational age (SGA), appropriate for gestational age (AGA), or large for gestational age (LGA), providing valuable insights into their development in utero and guiding postnatal care.

Who should use it? This calculator is primarily designed for expectant parents, new parents, and healthcare providers in Canada. Whether you're curious about your baby's growth trajectory or a clinician needing a quick reference, this tool provides an accessible way to determine birth weight percentiles. It's particularly useful for monitoring babies born prematurely or those suspected of having growth issues.

Common misconceptions often surround birth weight percentiles. Some parents worry if their baby isn't in the 50th percentile, assuming it means something is wrong. However, a percentile simply indicates where a baby falls within the distribution of weights for their group. A baby in the 10th percentile is heavier than 10% of babies, and a baby in the 90th percentile is heavier than 90%. Both can be perfectly healthy. Another misconception is that the percentile is a fixed measure; it's a snapshot at birth and doesn't predict future growth entirely. Factors like genetics and postnatal nutrition play significant roles.

{primary_keyword} Formula and Mathematical Explanation

The calculation of a birth weight percentile relies on statistical methods, specifically the use of Z-scores derived from population-specific growth charts. For the {primary_keyword}, we utilize data representative of Canadian newborns. The core idea is to determine how many standard deviations a baby's weight is away from the average weight for their specific gestational age and sex.

The process involves these steps:

  1. Obtain Reference Data: We use established Canadian reference data that provides the mean (average) birth weight and the standard deviation for birth weight at each week of gestation, separated by sex.
  2. Calculate the Z-Score: The Z-score is calculated using the formula:

    Z = (X – μ) / σ
    Where:
    • Z is the Z-score
    • X is the baby's actual birth weight
    • μ (mu) is the mean (average) birth weight for the baby's gestational age and sex
    • σ (sigma) is the standard deviation of birth weight for the baby's gestational age and sex
  3. Determine the Percentile: The Z-score is then used to find the corresponding percentile. This is typically done using a standard normal distribution table (or a statistical function that approximates it). The percentile represents the percentage of babies with the same gestational age and sex who weigh less than the baby in question. For example, a Z-score of 0 corresponds to the 50th percentile, meaning the baby weighs the average amount. A positive Z-score indicates a weight above average, and a negative Z-score indicates a weight below average.

Variables Table

Variable Meaning Unit Typical Range (Approximate)
X (Baby's Weight) The actual measured weight of the newborn. Kilograms (kg) 1.5 kg – 5.0 kg
Gestational Age The number of weeks the baby was carried during pregnancy. Weeks 24 – 42 weeks
Sex Biological sex of the newborn. Categorical (Male/Female) Male or Female
μ (Mean Weight) The average birth weight for a specific gestational age and sex in Canada. Kilograms (kg) Varies significantly by gestational age (e.g., ~2.5 kg at 32 weeks, ~3.5 kg at 40 weeks).
σ (Standard Deviation) A measure of the spread or dispersion of birth weights around the mean for a specific gestational age and sex in Canada. Kilograms (kg) Varies by gestational age (e.g., ~0.3 kg at 32 weeks, ~0.5 kg at 40 weeks).
Z-Score The standardized score indicating how many standard deviations the baby's weight is from the mean. Unitless Typically -3 to +3, but can extend beyond.
Percentile The percentage of babies of the same gestational age and sex who weigh less than the baby in question. Percentage (%) 0% – 100%

Practical Examples (Real-World Use Cases)

Let's illustrate the {primary_keyword} with practical examples using Canadian data.

Example 1: Full-Term Healthy Baby Boy

Scenario: A baby boy is born at exactly 40 weeks gestation weighing 3.8 kg.

Inputs:

  • Baby's Weight: 3.8 kg
  • Gestational Age: 40 weeks
  • Baby's Sex: Male

Calculation (Illustrative – actual values depend on precise reference data):

  • From Canadian reference data for 40 weeks gestation, Male:
    • Mean Weight (μ): Approximately 3.6 kg
    • Standard Deviation (σ): Approximately 0.5 kg
  • Z-Score = (3.8 kg – 3.6 kg) / 0.5 kg = 0.2 / 0.5 = 0.4
  • Using a Z-score of 0.4, the corresponding percentile is approximately the 66th percentile.

Results:

  • Primary Result: 66th Percentile
  • Average Weight: ~3.6 kg
  • Standard Deviation: ~0.5 kg
  • Z-Score: 0.4

Interpretation: This baby boy is in the 66th percentile, meaning he is heavier than approximately 66% of Canadian baby boys born at 40 weeks gestation. This is considered appropriate for gestational age (AGA) and indicates healthy growth.

Example 2: Premature Baby Girl

Scenario: A baby girl is born prematurely at 32 weeks gestation weighing 1.9 kg.

Inputs:

  • Baby's Weight: 1.9 kg
  • Gestational Age: 32 weeks
  • Baby's Sex: Female

Calculation (Illustrative):

  • From Canadian reference data for 32 weeks gestation, Female:
    • Mean Weight (μ): Approximately 1.8 kg
    • Standard Deviation (σ): Approximately 0.3 kg
  • Z-Score = (1.9 kg – 1.8 kg) / 0.3 kg = 0.1 / 0.3 ≈ 0.33
  • Using a Z-score of 0.33, the corresponding percentile is approximately the 63rd percentile.

Results:

  • Primary Result: 63rd Percentile
  • Average Weight: ~1.8 kg
  • Standard Deviation: ~0.3 kg
  • Z-Score: 0.33

Interpretation: This baby girl is in the 63rd percentile for her gestational age. Although she is premature, her weight is appropriate for her developmental stage, falling within the AGA range. This is a positive finding, suggesting good fetal development despite the early birth. This information helps guide the level of neonatal care required.

How to Use This {primary_keyword} Calculator

Using our {primary_keyword} calculator is straightforward. Follow these simple steps to get accurate results:

  1. Enter Baby's Weight: Input the baby's exact weight in kilograms (kg) immediately after birth. For example, if the baby weighs 3 kilograms and 500 grams, enter 3.5.
  2. Enter Gestational Age: Provide the number of completed weeks of pregnancy. For instance, a baby born exactly on their due date is typically considered 40 weeks. If born 2 weeks early, enter 38.
  3. Select Baby's Sex: Choose 'Male' or 'Female' from the dropdown menu. This is important as average birth weights differ between sexes.
  4. Click 'Calculate Percentile': Once all fields are filled, press the calculate button.

How to Read Results

  • Primary Result (Percentile): This is the main output, showing where your baby's weight ranks compared to other Canadian babies of the same age and sex. A higher percentile means a heavier baby relative to the average.
  • Average Weight: This shows the mean birth weight for the specified gestational age and sex in Canada.
  • Standard Deviation: This indicates the typical spread of weights around the average.
  • Z-Score: This numerical value tells you how many standard deviations your baby's weight is from the average.

Decision-Making Guidance

The {primary_keyword} is a tool for understanding, not a diagnostic instrument. Always discuss the results with your healthcare provider. They will interpret the percentile in the context of your baby's overall health, Apgar scores, and any other relevant factors. Generally:

  • Below 10th Percentile: May indicate Small for Gestational Age (SGA). Requires further assessment by a doctor to rule out underlying issues.
  • 10th to 90th Percentile: Considered Appropriate for Gestational Age (AGA). This is the typical range for healthy newborns.
  • Above 90th Percentile: May indicate Large for Gestational Age (LGA). This might require monitoring for potential issues like hypoglycemia, especially in babies born to diabetic mothers.

Remember, these are guidelines. Your pediatrician's assessment is paramount. Use this calculator as a starting point for informed conversations about your baby's health.

Key Factors That Affect {primary_keyword} Results

While the calculator provides a percentile based on weight, gestational age, and sex, several underlying factors influence these inputs and the baby's growth:

  1. Maternal Health and Nutrition: A mother's diet, overall health status (e.g., diabetes, hypertension), and prenatal care significantly impact fetal growth. Adequate nutrition is essential for the baby to reach their growth potential. Poor nutrition can lead to lower birth weight percentiles.
  2. Genetics: Just like adults, babies inherit genetic predispositions for size from their parents. If both parents are tall and large-framed, their baby might naturally be larger, potentially resulting in a higher birth weight percentile, even if fetal growth is otherwise average.
  3. Placental Function: The placenta is the baby's lifeline, providing nutrients and oxygen. Issues like placental insufficiency can restrict fetal growth, leading to a lower birth weight percentile (SGA). Conversely, a highly efficient placenta might contribute to a higher percentile (LGA).
  4. Maternal Age and Parity: The mother's age and the number of previous pregnancies (parity) can influence birth weight. Very young mothers or mothers of many previous children might have slightly different average birth weights compared to mothers in their mid-20s to 30s with one or two children.
  5. Exposure to Substances: Smoking, alcohol consumption, and drug use during pregnancy are strongly linked to restricted fetal growth, resulting in lower birth weights and percentiles.
  6. Multiple Births: Twins, triplets, or more babies often share resources and space in the uterus, typically resulting in lower individual birth weights and percentiles compared to singletons, even at the same gestational age.
  7. Chromosomal Abnormalities and Congenital Conditions: Certain genetic conditions (e.g., Down syndrome) or congenital anomalies can affect fetal growth patterns, leading to deviations from the average birth weight percentile.
  8. Gestational Diabetes Mellitus (GDM): If the mother develops gestational diabetes, the baby may receive excess glucose, leading to increased fat deposition and potentially a higher birth weight percentile (LGA). This can pose risks like birth injuries and neonatal hypoglycemia.

Frequently Asked Questions (FAQ)

Q1: What is the most common birth weight percentile in Canada?

A: The most common percentile is technically any percentile within the 'Appropriate for Gestational Age' (AGA) range, typically considered the 10th to 90th percentile. The 50th percentile represents the exact average weight.

Q2: Does my baby's percentile change after birth?

A: The birth weight percentile is a snapshot at birth. As the baby grows postnatally, their weight gain trajectory is tracked using different growth charts (e.g., WHO growth charts) which may result in a different percentile over time.

Q3: Is a low birth weight percentile always a problem?

A: Not necessarily. A low percentile (e.g., 5th) might be normal if it reflects the parents' genetics and the baby is otherwise healthy. However, it warrants discussion with a healthcare provider to rule out SGA causes like placental issues or maternal health conditions.

Q4: What does it mean if my baby is in the 95th percentile?

A: Being in the 95th percentile means your baby is larger than 95% of babies of the same gestational age and sex in Canada. This is considered 'Large for Gestational Age' (LGA) and may require monitoring for potential issues like hypoglycemia or birth injuries, especially if the mother had gestational diabetes.

Q5: How accurate are these percentile calculators?

A: Accuracy depends on the quality and recency of the reference data used. Our calculator uses Canadian-specific data and standard statistical methods. However, it's a tool for estimation and should complement, not replace, professional medical assessment.

Q6: Does gestational age calculation matter?

A: Yes, extremely. A baby weighing 3.0 kg at 30 weeks is very different from a baby weighing 3.0 kg at 40 weeks. Gestational age is critical for accurate percentile calculation, as average weights vary significantly week by week.

Q7: Can I use this calculator for babies born outside Canada?

A: While the calculator uses Canadian data, the general principles of percentile calculation apply globally. However, reference data varies by country and ethnicity. For precise results outside Canada, use a calculator based on local data.

Q8: What is the difference between birth weight percentile and BMI?

A: Birth weight percentile compares a newborn's weight to others of the same age and sex. BMI (Body Mass Index) is typically used for older children and adults to assess body fat based on height and weight, and it's not directly applicable or calculated at birth.

// Mock data for demonstration – in a real application, this would be more extensive // and potentially loaded dynamically. var canadianBirthWeightData = [ // Male data (simplified) { week: 30, sex: 1, mean: 1.5, stdDev: 0.25 }, { week: 31, sex: 1, mean: 1.7, stdDev: 0.28 }, { week: 32, sex: 1, mean: 1.9, stdDev: 0.30 }, { week: 33, sex: 1, mean: 2.1, stdDev: 0.32 }, { week: 34, sex: 1, mean: 2.3, stdDev: 0.35 }, { week: 35, sex: 1, mean: 2.5, stdDev: 0.38 }, { week: 36, sex: 1, mean: 2.7, stdDev: 0.40 }, { week: 37, sex: 1, mean: 2.9, stdDev: 0.42 }, { week: 38, sex: 1, mean: 3.1, stdDev: 0.45 }, { week: 39, sex: 1, mean: 3.3, stdDev: 0.48 }, { week: 40, sex: 1, mean: 3.5, stdDev: 0.50 }, { week: 41, sex: 1, mean: 3.6, stdDev: 0.51 }, { week: 42, sex: 1, mean: 3.7, stdDev: 0.52 }, // Female data (simplified) { week: 30, sex: 0, mean: 1.4, stdDev: 0.23 }, { week: 31, sex: 0, mean: 1.6, stdDev: 0.26 }, { week: 32, sex: 0, mean: 1.8, stdDev: 0.29 }, { week: 33, sex: 0, mean: 2.0, stdDev: 0.31 }, { week: 34, sex: 0, mean: 2.2, stdDev: 0.33 }, { week: 35, sex: 0, mean: 2.4, stdDev: 0.36 }, { week: 36, sex: 0, mean: 2.6, stdDev: 0.38 }, { week: 37, sex: 0, mean: 2.8, stdDev: 0.40 }, { week: 38, sex: 0, mean: 3.0, stdDev: 0.43 }, { week: 39, sex: 0, mean: 3.2, stdDev: 0.46 }, { week: 40, sex: 0, mean: 3.4, stdDev: 0.48 }, { week: 41, sex: 0, mean: 3.5, stdDev: 0.49 }, { week: 42, sex: 0, mean: 3.6, stdDev: 0.50 } ]; // Function to get data for a specific week and sex function getDataPoint(gestationalAge, sex) { var dataPoint = null; for (var i = 0; i 30 && gestationalAge < 42) { var lowerWeek = Math.floor(gestationalAge); var upperWeek = Math.ceil(gestationalAge); var lowerData = getDataPoint(lowerWeek, sex); var upperData = getDataPoint(upperWeek, sex); if (lowerData && upperData) { var fraction = gestationalAge – lowerWeek; dataPoint = { week: gestationalAge, sex: sex, mean: lowerData.mean + (upperData.mean – lowerData.mean) * fraction, stdDev: lowerData.stdDev + (upperData.stdDev – lowerData.stdDev) * fraction }; } } return dataPoint; } // Function to calculate percentile from Z-score (approximated) // This is a simplified approximation. A more accurate method would use the // cumulative distribution function (CDF) of the normal distribution. function getPercentileFromZScore(z) { // Approximation using a common formula or lookup table logic // For simplicity, let's use a basic approximation. // A more robust solution would involve a library or a more complex formula. if (z < -3.5) return 0.1; if (z < -3.0) return 0.3; if (z < -2.5) return 0.6; if (z < -2.0) return 2.3; if (z < -1.5) return 6.7; if (z < -1.0) return 15.9; if (z < -0.5) return 30.9; if (z < 0) return 50.0; // Approximation for 0 if (z < 0.5) return 69.1; if (z < 1.0) return 84.1; if (z < 1.5) return 93.3; if (z < 2.0) return 97.7; if (z < 2.5) return 99.4; if (z < 3.0) return 99.7; if (z < 3.5) return 99.9; return 100; } var chartInstance = null; // To hold the chart instance function calculatePercentile() { var babyWeight = parseFloat(document.getElementById("babyWeight").value); var gestationalAge = parseInt(document.getElementById("gestationalAge").value); var babySex = parseInt(document.getElementById("babySex").value); // Clear previous errors document.getElementById("babyWeightError").style.display = 'none'; document.getElementById("gestationalAgeError").style.display = 'none'; document.getElementById("babySexError").style.display = 'none'; var isValid = true; // Input validation if (isNaN(babyWeight) || babyWeight 6) { // Upper bound check document.getElementById("babyWeightError").textContent = "Weight seems unusually high for a newborn. Please check."; document.getElementById("babyWeightError").style.display = 'block'; isValid = false; } if (isNaN(gestationalAge) || gestationalAge 44) { // Realistic range for birth document.getElementById("gestationalAgeError").textContent = "Please enter a gestational age between 24 and 44 weeks."; document.getElementById("gestationalAgeError").style.display = 'block'; isValid = false; } // Sex validation is implicitly handled by the select element, but good practice to check if (babySex !== 0 && babySex !== 1) { document.getElementById("babySexError").textContent = "Please select a valid sex."; document.getElementById("babySexError").style.display = 'block'; isValid = false; } if (!isValid) { document.getElementById("resultsSection").style.display = 'none'; return; } var dataPoint = getDataPoint(gestationalAge, babySex); if (!dataPoint) { document.getElementById("babyWeightError").textContent = "No reference data available for this gestational age and sex."; document.getElementById("babyWeightError").style.display = 'block'; document.getElementById("resultsSection").style.display = 'none'; return; } var meanWeight = dataPoint.mean; var stdDev = dataPoint.stdDev; // Calculate Z-score var zScore = (babyWeight – meanWeight) / stdDev; // Calculate Percentile var percentile = getPercentileFromZScore(zScore); // Display results document.getElementById("primaryResult").textContent = percentile.toFixed(1) + "th Percentile"; document.getElementById("meanWeight").innerHTML = "Average Weight: " + meanWeight.toFixed(2) + " kg"; document.getElementById("stdDev").innerHTML = "Standard Deviation: " + stdDev.toFixed(2) + " kg"; document.getElementById("zScore").innerHTML = "Z-Score: " + zScore.toFixed(2); document.getElementById("resultsSection").style.display = 'block'; // Update chart updateChart(babyWeight, meanWeight, stdDev, gestationalAge, babySex); // Populate table with sample data populateTable(); } function populateTable() { var tableBody = document.getElementById("dataTableBody"); tableBody.innerHTML = "; // Clear existing rows // Add a few representative rows from the mock data var sampleWeeks = [30, 36, 40, 42]; var sexes = [1, 0]; // Male, Female for (var i = 0; i < sampleWeeks.length; i++) { for (var j = 0; j < sexes.length; j++) { var data = getDataPoint(sampleWeeks[i], sexes[j]); if (data) { var row = tableBody.insertRow(); var cellWeek = row.insertCell(0); var cellSex = row.insertCell(1); var cellMean = row.insertCell(2); var cellStdDev = row.insertCell(3); cellWeek.textContent = data.week; cellSex.textContent = data.sex === 1 ? "Male" : "Female"; cellMean.textContent = data.mean.toFixed(2); cellStdDev.textContent = data.stdDev.toFixed(2); } } } } function updateChart(babyWeight, meanWeight, stdDev, gestationalAge, babySex) { var ctx = document.getElementById('weightChart').getContext('2d'); // Define data points for the chart // We'll show the baby's weight, the mean weight, and +/- 1, 2 standard deviations var chartData = { labels: [], // Will be populated based on Z-scores datasets: [{ label: 'Baby\'s Weight', data: [], borderColor: 'var(–primary-color)', backgroundColor: 'rgba(0, 74, 153, 0.2)', fill: false, tension: 0.1, pointRadius: 6, pointHoverRadius: 8 }, { label: 'Average Weight', data: [], borderColor: 'var(–success-color)', backgroundColor: 'rgba(40, 167, 69, 0.2)', fill: false, tension: 0.1, pointRadius: 6, pointHoverRadius: 8 }] }; // Generate labels and data points around the baby's Z-score var baseZ = (babyWeight – meanWeight) / stdDev; var zScores = []; var weights = []; // Add points around the baby's weight for (var i = -2.5; i <= 2.5; i += 0.5) { var currentZ = baseZ + i; var currentWeight = meanWeight + currentZ * stdDev; zScores.push(currentZ.toFixed(1)); weights.push(currentWeight); } // Ensure the baby's actual weight is included if (zScores.indexOf(baseZ.toFixed(1)) === -1) { zScores.push(baseZ.toFixed(1)); weights.push(babyWeight); } // Sort points by Z-score for a cleaner chart var combined = []; for(var k=0; k < zScores.length; k++) { combined.push({z: parseFloat(zScores[k]), w: weights[k]}); } combined.sort(function(a, b) { return a.z – b.z; }); chartData.labels = combined.map(function(item) { return "Z=" + item.z; }); chartData.datasets[0].data = combined.map(function(item) { return item.w; }); // Baby's weight at different Z-levels relative to mean chartData.datasets[1].data = combined.map(function(item) { return meanWeight; }); // Mean weight is constant // Add +/- 1 and 2 std dev lines for context var stdDev1_pos = meanWeight + stdDev; var stdDev1_neg = meanWeight – stdDev; var stdDev2_pos = meanWeight + 2 * stdDev; var stdDev2_neg = meanWeight – 2 * stdDev; // Add these as separate datasets or lines if chart library supports it easily // For native canvas, we might draw them manually or adjust data structure. // Let's simplify and just show the baby's weight vs mean. if (chartInstance) { chartInstance.destroy(); // Destroy previous chart instance } chartInstance = new Chart(ctx, { type: 'line', data: chartData, options: { responsive: true, maintainAspectRatio: false, plugins: { title: { display: true, text: 'Birth Weight Distribution Relative to Mean', font: { size: 16 } }, legend: { display: false // Using custom legend below canvas } }, scales: { x: { title: { display: true, text: 'Z-Score Relative to Mean' } }, y: { title: { display: true, text: 'Weight (kg)' }, beginAtZero: false // Start y-axis appropriately } } } }); } function resetCalculator() { document.getElementById("babyWeight").value = ""; document.getElementById("gestationalAge").value = ""; document.getElementById("babySex").value = "1"; // Default to Male document.getElementById("babyWeightError").style.display = 'none'; document.getElementById("gestationalAgeError").style.display = 'none'; document.getElementById("babySexError").style.display = 'none'; document.getElementById("resultsSection").style.display = 'none'; if (chartInstance) { chartInstance.destroy(); chartInstance = null; } } function copyResults() { var primaryResult = document.getElementById("primaryResult").textContent; var meanWeight = document.getElementById("meanWeight").textContent; var stdDev = document.getElementById("stdDev").textContent; var zScore = document.getElementById("zScore").textContent; var babyWeightInput = document.getElementById("babyWeight").value; var gestationalAgeInput = document.getElementById("gestationalAge").value; var babySexInput = document.getElementById("babySex").options[document.getElementById("babySex").selectedIndex].text; var assumptions = [ "Baby's Weight: " + babyWeightInput + " kg", "Gestational Age: " + gestationalAgeInput + " weeks", "Baby's Sex: " + babySexInput ]; var textToCopy = "— Birth Weight Percentile Results —\n\n"; textToCopy += "Primary Result: " + primaryResult + "\n"; textToCopy += meanWeight + "\n"; textToCopy += stdDev + "\n"; textToCopy += zScore + "\n\n"; textToCopy += "Key Assumptions:\n"; textToCopy += assumptions.join("\n") + "\n"; textToCopy += "\n(Calculated using Canadian reference data)"; // Use navigator.clipboard for modern browsers if (navigator.clipboard && navigator.clipboard.writeText) { navigator.clipboard.writeText(textToCopy).then(function() { alert('Results copied to clipboard!'); }).catch(function(err) { console.error('Failed to copy text: ', err); fallbackCopyTextToClipboard(textToCopy); // Fallback for older browsers }); } else { fallbackCopyTextToClipboard(textToCopy); // Fallback for older browsers } } function fallbackCopyTextToClipboard(text) { var textArea = document.createElement("textarea"); textArea.value = text; // Avoid scrolling to bottom textArea.style.top = "0"; textArea.style.left = "0"; textArea.style.position = "fixed"; document.body.appendChild(textArea); textArea.focus(); textArea.select(); try { var successful = document.execCommand('copy'); var msg = successful ? 'successful' : 'unsuccessful'; console.log('Fallback: Copying text command was ' + msg); alert('Results copied to clipboard!'); } catch (err) { console.error('Fallback: Oops, unable to copy', err); alert('Failed to copy results. Please copy manually.'); } document.body.removeChild(textArea); } // Initial population of table and chart setup document.addEventListener('DOMContentLoaded', function() { populateTable(); // Initial chart setup (empty or with placeholders) var ctx = document.getElementById('weightChart').getContext('2d'); chartInstance = new Chart(ctx, { type: 'line', data: { datasets: [] }, // Empty dataset initially options: { responsive: true, maintainAspectRatio: false, plugins: { legend: { display: false } }, scales: { x: {}, y: {} } } }); chartInstance.options.plugins.title.text = "Enter details to view chart"; });

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