Biological variation of glycated haemoglobin in a paediatric population and its application to calculation of significant change between results
Bibliographic record
Abstract
BACKGROUND: To determine precisely the probability that a change between two glycated haemoglobin A1c (HbA1c) results is significant and that clinical actions may be required, the biological variation of HbA1c must be known. However, it has not been evaluated in a paediatric population. We therefore determined the long-term biological variation of HbA1c in a paediatric population and used it to generate a probability curve for significant changes between two consecutive HbA1c measurements. METHODS: A group of 24 boys and 14 girls with cystic fibrosis (CF) but without diabetes or impaired glucose tolerance has been selected. HbA1c has been measured at least five times over five consecutive years for all subjects. We have used the Fraser and Harris method to calculate within-subject biological variation (CV(I)), which allowed the determination of the probability that a change is significant between results. RESULTS: As within-subject variances are equivalent for girls and boys (P > 0.1), both genders were merged for biological variation analysis. The CV(I) calculated for HbA1c was 4.8% and the between-subject variation (CV(G)) was 12.8%. Then, a probability curve based on the CV(I) found was generated and showed that a change of 14% between two consecutive HbA1c results corresponding to a probability of 95% was significant. CONCLUSIONS: We determined for the first time the biological variation of HbA1c in a paediatric population, which is higher than the ones found for adult populations. The probability curves generated from these data could be invaluable tools for clinicians to balance HbA1c results with other clinical parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".