Identifying the body mass index of children awaiting dental surgery under general anaesthetic: an audit
Bibliographic record
Abstract
Children with overweight or obesity are at risk of experiencing perioperative complications during general anaesthesia (GA). At Guy's and St Thomas' NHS Foundation Trust in London, children who require dental surgery under GA are placed on a waiting list for the Dental Day Surgery Unit (DDSU) or the Evelina London Children's Hospital (ELCH), which has inpatient beds and a paediatric intensive care unit, depending on their body mass index (BMI) and centile thresholds. The waiting list for the ELCH is longer than for the DDSU. This article discusses the results of a retrospective audit which involved analysis of the BMI of 300 children (aged ≤16 years) on the waiting lists for both sites (DDSU n =250; ELCH n =50). The aims were to identify those who were overweight or very overweight, calculate how much weight loss would be required for some of those allocated to the ELCH to be treated instead at the DDSU and to achieve a healthy weight, and to compare obesity prevalence with national data. The results identified 57 (19%) of the 300 patients as very overweight or overweight. A total of 24 (48%) patients on the ELCH waiting list ( n =50) were identified as very overweight or overweight. For seven (29%) of these 24 patients, the amount of weight loss required to be treated at the DDSU ranged between 19.5kg and 0.9kg and the amount of weight loss required to attain a healthy weight ranged between 28.5kg and 11.5 kg. The prevalence of obesity among the audit cohort was lower than national obesity prevalence rates for children.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".