Severe obesity and global developmental delay in preschool children: Findings from a Canadian Paediatric Surveillance Program study
Bibliographic record
Abstract
Background: The co-presentation of severe obesity (SO) and global developmental delay (GDD) in Canadian preschool children has not been examined. However, SO and GDD may require syndromic diagnoses and unique management considerations. Objectives: To determine (1) minimum incidence; (2) age of onset and risk factors; and (3) health care utilization for co-presenting SO and GDD. Methods: Through the Canadian Paediatric Surveillance Program (CPSP), a monthly form was distributed to participants from February 2018 to January 2020 asking for reports of new cases of SO and GDD among children ≤5 years of age. We performed descriptive statistics for quantitative questions and qualitative content analysis for open-ended questions. Results: Forty-seven cases (64% male; 51% white; mean age: 3.5 ± 1.2 years) were included. Age of first weight concern was 2.5 ± 1.3 years and age of GDD diagnosis was 2.7 ± 1.4 years. Minimum incidence of SO and GDD was 3.3 cases per 100,000 for ≤5 years of age per year. Identified problems included school and/or behavioural problems (n = 17; 36%), snoring (n = 14; 30%), and asthma/recurrent wheeze (n = 10; 21%). Mothers of 32% of cases (n = 15) had obesity and 21% of cases (n = 10) received neonatal intensive care. Microarray was ordered for 57% (n = 27) of children. A variety of clinicians and services were accessed. As reported by CPSP participants, challenges faced by families and health service access were barriers to care. Conclusion: Children with SO and GDD have multiple comorbidities, and require early identification and referral to appropriate services. These cases may also benefit from additional testing to rule out known genetic obesity syndromes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".