Weight up? Changes in children’s anthropometry from time of referral to baseline assessment for paediatric weight management
Bibliographic record
Abstract
To examine children’s wait time to access a multidisciplinary, tertiary-level weight management clinic and assess anthropometric changes from time of referral to baseline assessment. A retrospective medical record review was completed of children (5 to 17 years) enrolled in a multidisciplinary, tertiary-level paediatric weight management clinic from 2006 to 2015. Children’s demographic and anthropometric data from their referral to and baseline assessment at the clinic were retrieved from medical records. Based on changes in body mass index (BMI) z-score from the time of referral to baseline assessment, children were categorized as decreasers (>0.05 unit decrease), increasers (>0.05 unit increase) or stabilizers (−0.05 to 0.05 unit change). The proportion of children with a ≥0.25 unit BMI z-score reduction was calculated. Analysis of variance and chi-squared tests were performed. Children (n=400) were 11.7 ± 2.9 years old at the time of referral, 52.8% (n=211) female, and had an average wait time of 4.5 ± 3.9 months. By 3 and 6 months postreferral, 44.0% (n=176) and 80.8% (n=323), respectively, had attended baseline assessments. Based on BMI z-score change, children were classified as decreasers (n=183; 45.8%), increasers (n=118; 29.5%) or stabilizers (n=99; 24.8%). One-fifth of children (n=86; 21.5%) experienced a BMI z-score reduction ≥0.25 units, a subgroup that was younger, had a higher BMI z-score at referral, and had a longer wait time between referral and baseline assessment (all P<0.05). Most children who enrolled in paediatric weight management initiated treatment within six months and experienced a modest decrease or stabilization in BMI z-score during their wait time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".