Bibliographic record
Abstract
The number of injury-related hospital admissions in Ontario declined by 10% during the latter half of the 1990s, dropping from more than 70 000 in 1995 to fewer than 65 000 in 1999. Despite the decrease, the 1999 figures still equate to 175 hospitalizations a day in Ontario. The data, from the Canadian Institute for Health Information, exclude injuries that were treated in emergency rooms, as well as patients who did not survive long enough to be hospitalized. The average age of patients admitted due to an injury increased from 50 to 53 between 1995 and 1999, but the average length of stay for these patients remained stable at about 9 days. In 1999, patients 65 and older accounted for 43% of all injury-related admissions. The Toronto region, with 456 injuries per 100 000 people, had the lowest rate in the province. The majority of injury-related hospitalizations are caused by falls (59%), followed by motor vehicle collisions (13%). Assault-related injuries comprised only 3% of the total, and declined by 23% between 1995 and 1999. Eighty percent of the assault-related injuries involved men, 65% of whom were under age 35. July is the most common month for injury-related admissions, although December is the month in which the greatest proportion of such admissions result in an in-hospital death. The most common time for an injury-related hospital admission is 9 pm. — Lynda Buske, Associate Director of Research, CMA
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.025 |
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".