Fall-related injuries in a low-income setting: Results from a pilot injury surveillance system in Rawalpindi, Pakistan
Bibliographic record
Abstract
This study assessed the characteristics and emergency care outcomes of fall-related injuries in Pakistan. This study included all fall-related injury cases presenting to emergency departments (EDs) of the three teaching hospitals in Rawalpindi city from July 2007 to June 2008. Out of 62,530 injury cases, 43.4% (N=27,109) were due to falls. Children (0-15 years) accounted for about two out of five of all fall-related injuries. Compared with women aged 16-45 years, more men of the same age group presented with fall-related injuries (50% vs. 42%); however, compared with men aged 45 years or more, about twice as many women of the same age group presented with fall-related injuries (16% vs. 9%, P<0.001). For each reported death due to falls (n=57), 43 more were admitted (n=2443, 9%), and another 423 were discharged from the EDs (n=24,142, 91%). Factors associated with death or inpatient admission were: aged 0-15 years (adjusted odds ratio [aOR]=1.35), aged 45 years or more (aOR=1.94), male gender (aOR=1.15), falls occurring at home (aOR=3.38), in markets (aOR=1.43), on work sites (aOR=4.80), and during playing activities (aOR=1.68). This ED-based surveillance study indicated that fall prevention interventions in Pakistan should target children, older adult women, homes, and work sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".