Vancouver winters: environmental influences on inpatient adult orthopaedic trauma demographics.
Bibliographic record
Abstract
OBJECTIVE: To compare the pattern of adult inpatient orthopaedic injuries admitted at three Vancouver hospitals following one of the worst winter snowstorms in the region with the preceding control winter period. METHODS: The surveillance study was conducted at the University of British Columbia, Vancouver, Canada, 2007 to 2010. Inpatient adult admissions for orthopaedic injuries at three hospitals were recorded, including age, gender, anatomic location of injury, type of fracture (open or closed), fixation method (internal versus external fixation), and length of acute care hospital stay. Comparisons between admissions during this weather pattern and admission during a previous winter with minimal snow were made. SPSS 19 was used for statistical analysis. RESULTS: Of the 511 patients admitted under Orthopaedic trauma service during the significant winter snowstorms of December 2008-January 2009, 100 (19.6%) (Cl: 16.2%-23.2%) were due to ice and snow, whereas in the preceding mild winter only 18 of 415 (4.3%) (CI: 2.5%-6.8%) cases were related to snow (p < 0.05). Ankle and wrist fractures were the most frequent injuries during the index snow storm period (p < 0.05). At all the three institutions, 97 (96.5%) fractures were closed during the snowstorm as opposed to 17 (95%) during the control winter period. Internal fixation in 06 (89%) fractures as opposed to external fixation in 12 (11%) patients was the predominant mode of fixation across the board during both time periods. CONCLUSION: The study demonstrated a significantly higher inpatient orthopaedic trauma volume during the snowstorm. More rigorous prospective studies need to be designed to gain further insight to solving these problems from a public health perspective.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".