Unintentional falls at home among young and middle-aged New Zealanders resulting in hospital admission or death: context and characteristics.
Bibliographic record
Abstract
AIM: This study investigates the characteristics and contexts of unintentional falls at home among young and middle-aged adults. METHOD: We conducted a population-based study of individuals aged 25-59 years resident in Auckland who were admitted to hospital or died following a non-occupational fall at home (July 2005-June 2006). Information was obtained from participant or proxy interviews, and reviews of inpatient records. RESULTS: 344 patients (including 1 death) met the study eligibility criteria representing an overall age-specific incidence rate of 54.0/100,000 (95% CI 48.6-60.1) for the 12-month period. Of the 335 cases (97.4%) interviewed, 36% fell on stairs/steps, 31% fell on the same level, 13% of falls involved ladders/scaffolding, and 11% fell from buildings/structures. Stairs or steps were involved in 43% of falls among females and 28% of falls among males. The majority of falls (81%) occurred in the individual's own home. A quarter (24%) of participants had consumed >or= 2 drinks in the 6 hours preceding the fall, and 24% were on >or= 2 prescription medications. CONCLUSION: While this study was not designed to identify the specific causes of falls, the findings reveal several important contextual factors that can be targeted to prevent fatal and serious non-fatal falls at home among this age group.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".