Management of Injuries to the Anterior Cruciate Ligament: Results of a Survey of Orthopaedic Surgeons in Canada
Bibliographic record
Abstract
OBJECTIVE: To identify the approaches to management of anterior cruciate ligament (ACL) injury by Canadian orthopedic surgeons. METHODS: A questionnaire was mailed to 234 physicians randomly chosen from the Canadian Orthopaedic Association directory to obtain the following information: 1) how orthopaedic surgeons diagnose acute hemarthroses; 2) how patients in any of three common ACL injury scenarios would be managed; 3) what variations exist in surgical technique; and 4) how patient variables such as age, gender, and alignment influence the decision-making process. RESULTS: The return rate was 72%, and 56% of respondents were from academic centers. Patients such as those described in the protocol are routinely managed by 80% of the respondents. The diagnosis of acute hemarthrosis is predominantly made by means of clinical examination and radiographs. Magnetic resonance imaging (MRI) is used occasionally by 43% and routinely by 6% of those who responded; arthroscopy is used routinely by 24%. For the competitive athlete with a complete ACL tear, 64% would recommend reconstruction and 33% would recommend bracing and rehabilitation. For reconstruction, 59% would use bone-patellar tendon-bone (B-PT-B) autograft and 32% would use hamstring tendon autograft; 40% would incorporate the ACL stump during reconstruction. Of the respondents, 77% would advocate ACL reconstruction for competitive athletes with chronic ACL injury. Of these, 63% would use B-PT-B autograft and 27% would use hamstring tendons. If bracing and rehabilitation failed, 98% would recommend ACL reconstruction. In ACL reconstruction, synthetic augmentation would be used by 12% in chronic cases and by 16% in acute cases. In making the decision to perform ACL reconstruction, 53% consider limb alignment to be important and 67% consider moderate patellofemoral pain to be important. Seventy-one percent are influenced by patellofemoral pain when choosing a surgical technique, with a trend toward semitendinosis autograft rather than B-PT-B autograft reconstruction. For the 8-year-old child with an acute ACL injury, 63% of the respondents would recommend rehabilitation and bracing. For the 14-year-old, 45% would recommend rehabilitation and bracing and 37% would recommend ACL reconstruction after physeal closure. CONCLUSION: The results of the survey indicate that, with respect to some of the issues, there is a wide variation in management of acute and chronic ACL injuries among Canadian orthopedic surgeons. Future research and randomized, controlled clinical trials should be directed toward these areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".