General practitioner diagnosis and management of acute knee injuries: summary of an evidence-based guideline.
Bibliographic record
Abstract
AIMS: To summarise evidence and key recommendations for general practitioner diagnosis and management of acute soft-tissue knee injuries, based on the New Zealand guideline. METHODS: A multidisciplinary team developed the guideline by critically appraising and grading retrieved literature using the Graphic Appraisal Tools for Epidemiology, Clinical decision rules and the Scottish Intercollegiate Guideline Network. Recommendations were derived from resulting evidence tables. RESULTS: For both diagnosis and management there is a paucity of good evidence to support diagnosis and treatment of internal derangements of the knee, hence some aspects of the guideline are guideline team consensus. Good evidence supports the use of the Ottawa Knee rules to guide decisions about the use of X-ray, and the Lachman test in diagnosing anterior cruciate ligament (ACL) tears. Evidence supports inclusion of proprioceptive training in rehabilitation programmes following ACL reconstruction and in people with ACL-deficient knees. There is good evidence that ultrasound is of little benefit, and there is no evidence that physiotherapy be routinely advocated following meniscectomy. CONCLUSION: This guideline provides an evidence-based framework for diagnosis and management of internal derangements of the knee following acute injury. Moreover, its development highlights significant gaps in the evidence base and identifies priorities for new research.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".