PP507 Topic: AS22–Quality and Safety/Errors/Data Management/Other: A REVIEW OF PAEDIATRIC BATHTUB DROWNINGS IN ONTARIO
Bibliographic record
Abstract
Aims & Objectives: According to eCHIRPP, bathtub drownings make up a large portion of water-related deaths, accounting for 9% of fatalities between 2004-2013. This study aims to determine the incidence of fatal paediatric bathtub drownings in Ontario and provide a descriptive analysis of patient and household demographics, and circumstantial factors characterising the events. Methods: This study was a descriptive analysis of a case series of accidental paediatric bathtub drownings resulting in death within the province of Ontario between 2003-2022. Inclusion criteria were any child, aged 0-18 years, whose cause of death was deemed secondary to drowning in a bathtub. Results: A total of 47 cases met inclusion criteria. 55% were aged 0-24 months (0-18 years). 38% had a known seizure disorder, 15% had developmental delay. 43% occurred within the spring time. Drowning location was home residence in 92% of total cases. Lapse of supervision was reported in all 47 cases (100%). Reasons for supervision lapse included: making food or preparing a bottle (11%); getting pyjamas or towels (9%); attending to sibling (6%), unwitnessed re-entry into an undrained bathtub. 55% were cobathing, median bathwater height was 6 inches. Bath seats were used in 9% of cases. Conclusions: This study is the second provincial review of paediatric bathtub drownings over a 20-year period. With 47 cases analysed, it is the most comprehensive local paediatric dataset available at this time. These data suggest that public health campaigns could focus on the aforementioned risk factors to better inform the community of possible features associated with accidental paediatric bathtub drowning. Keywords: drowning, bathtub
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".