Analysis of serious injuries caused by stairway falls
Bibliographic record
Abstract
Abstract Objective: To study the demographics of patients who sustained serious injuries from falling down stairs. Method: This study is a retrospective analysis of all patients who presented to an urban hospital with an injury severity score of equal to or greater than 16 after falling down stairs during the period January 1992 to December 1996. Patient demographics, stair location and number, severity and anatomical nature of injuries, serum ethanol levels, patient referral characteristics and mortality rates were examined. Results: A total of 113 patients were studied; 74% were male. Sixty‐five per cent of falls occurred at the patient’s place of residence. Ethanol may have been a contributing factor in 55% of injuries. Ninety‐five per cent of victims had an injury severity score of equal to or less than 30. Head injuries were common with acute subdural haematoma occurring in 57% of patients. Head‐injured patients had a significantly higher mortality rate than non‐head‐injured patients (P = 0.02). Increasing age and higher injury severity score correlated with poor outcome in all patients. Thirty‐nine of the 113 stairway‐fall patients (35%) died in hospital compared with 207 of 1091 non‐stairway‐fall patients (19%) during the same period (P < 0.001). Conclusion: In the adult population, falls down stairs may result in serious injuries, particularly head trauma. In the present study, this group of patients had a significantly higher mortality than other trauma patients.
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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 | 0.002 |
| 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) | 0.002 | 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".