This was not an accident: An injury prevention primer for emergency and community nurses
Bibliographic record
Abstract
Early one morning in February, Alison, 18 months old, is in the kitchen with her father and two older sisters. While her father is getting the two older children ready for school, Alison reaches up and pulls on the tablecloth. Her father had set his coffee down near the edge of the table; the cup is pulled off. The hot liquid lands on the toddler and scalds parts of her face, shoulder and arm. Alison is taken to the emergency room, where she is treated for second-degree burns. Julian, aged three, is playing in his bedroom while his parents carry grocery bags from the car to the kitchen. After a few minutes, Julian doesn’t answer their calls. He is no longer in his room. He has slipped through the patio door into the pool, to which he gained access via the elevated deck attached to the house. It is only the beginning of May and the water is freezing. Julian is resuscitated and rushed to the emergency department. He is admitted to the intensive care unit where his condition is listed as critical.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".