Delay to orthopedic consultation for isolated limb injury
Bibliographic record
Abstract
OBJECTIVE To describe referral mechanisms for referral to orthopedic surgery for isolated limb injuries in a public health care system and to identify factors affecting access. DESIGN Cross-sectional survey. SETTING Orthopedic surgery service in a level 1 trauma centre in Montreal, Que. PARTICIPANTS We conducted a prospective study of 166 consecutive adults (mean age 48 years) referred to orthopedic surgery for isolated limb injuries during a 4-month period. MAIN OUTCOME MEASURES Self-reported data on the nature of the trauma, the elapsed time between injury and orthopedic consultation, the number and type of previous primary care consultations, sociodemographic characteristics, and the level of satisfaction with care. RESULTS Average time between the injury and orthopedic consultation was 89 hours (range 3 to 642), with an average of 68 hours (range 0 to 642) for delay between primary care consultation and orthopedic consultation. A total of 36% of patients with time-sensitive diagnoses had unacceptable delays to orthopedic consultation according to the Quebec Orthopaedic Association guidelines. Lower limb injury, consulting first at another hospital, living far from the trauma centre, patient perception of low severity, and having a soft tissue injury were associated with longer delays. CONCLUSION Identifying gaps and risk factors for slower access might help improve referral mechanisms for orthopedic consultation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".