Primary care management for isolated limb injury: Referral to orthopedic surgery in a trauma center
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal injuries affect up to 13% of adults annually. Despite this high incidence, quality of primary care, including analgesia, may be sub-optimal. The goal of this study is to describe the quality of primary care for ambulatory patients with isolated limb injury and to identify related factors. METHODS: A cross sectional study was undertaken prospectively on 166 consecutive ambulatory adult patients with isolated limb injury who presented to orthopedics service in a Level one Trauma Centre. Quality of care was assessed by evaluating analgesia, walking aids, immobilization, and quality of referral diagnosis according to actual expert recommendations. RESULTS: This study revealed low quality of primary care for 82 (53.2%) of injured patients. Seventy-three patients (50.3%) had pain level over 5/10 and 45 cases (28.5%) had insufficient/absent analgesia prescriptions. Fifty-one (32.3%) had unacceptable immobilization and 21/59 (35.6%) patients with a lower limb injury did not receive a walking aid prescription. A total of 61 patients (36.7%) had an absent or inadequate referral diagnosis. Factors associated with lower quality depended on the specific quality indicator and included: living further away from the hospital, younger age, initially consulting at another health care center, having a fracture, and being a smoker. CONCLUSION: The high frequency of low quality of care underlines the necessity for orthopedic surgeons to be involved in primary care education. Identifying factors associated with lower quality of care will orient efforts to improve medical care of patients with isolated traumatic injury.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".