Paediatric obesity appears to lower the risk of diabetes if selection bias is ignored
Bibliographic record
Abstract
BACKGROUND: Frustrated with the onslaught of articles reporting fascination with results that appear paradoxical but are merely due to selection bias, we studied the apparent effect of obesity on diabetes risk in youth who had a test for diabetes. We hypothesised that obese subjects would have lower rates of diabetes than non-obese subjects due to selection bias, and consequently, obesity would appear to lower the risk of diabetes. METHODS: Retrospective cohort study of children (4-9 years), pre-teens (10-12 years) and teenagers (13-19 years). Participation was restricted to those who had a test of haemoglobin A1C along with measured height and weight. Body mass index percentile via the Centers for Disease Control and Prevention age and sex standards was calculated and categorised. The main outcome was A1C%, subsequently categorised at the level for diagnosis of diabetes mellitus (≥6.5%). RESULTS: The sample consisted of 134 (2%) underweight, 1718 (30%) healthy weight, 660 (12%) overweight and 3190 (56%) obese individuals. 16% (n=936) had an A1C≥6.5%. Overall, healthy weight children had 8.2 times the risk of A1C≥6.5% (95% CI 5.3 to 12.7) compared with those in the obese category. The relative risk was 13 in pre-teens (95% CI 8.5 to 20.0) and 3.9 in teenagers (95% CI 3.3 to 4.7). CONCLUSIONS: Healthy weight was associated with a 4-13 times higher relative risk of diabetes mellitus compared with being obese. While apparently shocking, the study's fatal flaw (selection bias) explains the 'paradoxical' finding. Ignoring selection bias can delay advances in medical science.
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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 | 0.091 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".