A descriptive study of traumatic head injury discharges in Newfoundland and Labrador, 1985-1998
Bibliographic record
Abstract
Background: In the last decade the number of traumatic head injuries (THIs) admitted to Newfoundland's main neurosurgical referral centers has noticeably declined, particularly those resulting from motor vehicle accidents (MVAs). Objectives: To describe the epidemiology of THIs over the period 1985-98 and to assess any relationship between the incidence of THIs and use of helmets, seatbelts and alcohol. To determine the occurrence of these preventative measures. Methods: The charts of 2,739 patients from the greater eastern area of Newfoundland were reviewed. Other etiological factors affecting the incidence of THIs such as animal attacks, fall, brawls, sports/play, abuse, pedestrians, moose-MVAs, plane crashes, explosions, suicide attempts, being struck with a foreign object and work injuries were also examined. Sex, age, dates of injury and admission, ICD codes assigned, physiological outcome, hospital outcome and geographic location of injury were also collected. Results: Falls accounted for 31.2 percent of all THIs; MVAs, the second most common cause, accounted for 27.4 percent of all THIs. Most injuries were incurred by people ages 15 and younger (26 %). They were typically male and lived in the urban eastern area of Newfoundland. Enforcement and legislation on helmet and seatbelt use and drinking and driving were temporally associated with the observed decline in THIs. A great deal of safety education and public awareness, however, is still needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".