Bibliographic record
Abstract
Bathtub drownings are a significant cause of mortality in the pediatric population. Infants and preambulatory children are disproportionately affected, and several studies have suggested that preventative campaigns have been ineffective in the prevention of such deaths. To obtain a better understanding of the factors associated with bathtub drownings, a retrospective review of autopsy records over a 20-year period (1984-2003) was performed. Eighteen consecutive cases of bathtub drownings were identified in 8 males and 10 females (ratio, 0.8; P = 0.6374). The age ranged from 6 months to 70 months (mean, 17 months; median, 11 months), and most cases occurred in infants aged 12 months or less (72%). Males tended to be older than females (mean, 23 months versus 11 months; P = 0.1889). Associated factors included inadequate adult supervision (89%), cobathing (39%), the use of infant bath seats (17%), and coexistent medical disorders predisposing the infant or child to the drowning episode (17%). The pathologic findings included a frothy exudate (28%), pleural effusion (28%), and increased lung weight (61%). All toxicologic samples submitted for analysis were negative. The present study highlights the factors associated with bathtub drownings and may aid in the prevention of such deaths in the pediatric population.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".