The use of early immobilization in the management of acute soft-tissue injuries of the knee: results of a survey of emergency physicians, sports medicine physicians and orthopedic surgeons
Bibliographic record
Abstract
BACKGROUND: Evidence-based guidelines on the use of immobilization in the management of common acute soft-tissue knee injuries do not exist. Our objective was to explore the practice patterns of emergency physicians (EPs), sports medicine physicians (SMPs) and orthopedic surgeons (OS) regarding the use of early immobilization in the management of these injuries. METHODS: We developed a web-based survey and sent it to all EPs, SMPs and OS in a Canadian urban centre. The survey was designed to assess the likelihood of prescribing immobilization and to evaluate factors associated with physicians from these 3 disciplines making this decision. RESULTS: The overall response rate was 44 of 112 (39%): 17 of 58 (29%) EPs, 7 of 15 (47%) SMPs and 20 of 39 (51%) OS. In cases of suspected meniscus injuries, 9 (50%) EPs indicated they would prescribe immobilization, whereas no SMPs and 1 (5%) OS would immobilize (p = 0.002). For suspected anterior cruciate ligament injuries, 13 (77%) EPs, 2 (29%) SMPs and 5 (25%) OS said they would immobilize (p = 0.005). For lateral collateral ligament injuries, 9 (53%) EPs, no SMPs and 6 (32%) OS would immobilize (p = 0.04). All respondents would prescribe immobilization for a grossly unstable knee. CONCLUSION: We found that EPs were are more likely to prescribe immobilization for certain acute soft-tissue knee injuries than SMPs and OS. The development of an evidenced- based guideline for the use of knee immobilization after acute soft-tissue injury may reduce practice variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".