Pivot shift as an outcome measure for ACL reconstruction: a systematic review
Bibliographic record
Abstract
PURPOSE: To identify and evaluate the evidence for the pivot shift test as an outcome measure following ACL reconstruction. Achieving rotatory control of the knee post anterior cruciate ligament (ACL) reconstruction has been shown to increase patient satisfaction, decrease functional instability and potentially delay the development of osteoarthritis. The pivot shift is able to assess this rotatory component of knee laxity and appears to have the potential to become a benchmark in gauging the success of ACL surgery. Multiple confounding factors and discrepancies in performing the maneuver itself however put its usefulness in question. Thus, the literature was reviewed to assess whether the pivot shift was able to correlate with final functional outcomes. METHODS: Two reviewers searched two databases (MEDLINE and EMBASE) for randomized control trials that involved anterior cruciate ligament reconstruction in the last 5 years. All non-clinical studies were excluded. A quality assessment of the included studies was performed using the Jadad scale by a reviewer. The number of studies using the Pivot Shift Test as well as the test's relationship with functional outcome was evaluated. RESULTS: The literature search yielded 274 studies, of which 65 papers were included. The average Jadad quality score for papers reporting pivot shift as an outcome measure was 2.4, with the most frequent score being 3. Forty seven of 65 studies described the Pivot Shift Test as an outcome measure following ACL reconstruction. Of the 47 studies that included pivot shift as an outcome measure, 40 (85%) correlated with the final functional outcomes. CONCLUSION: The pivot shift test is an important test following ACL reconstruction, and it correlates with functional outcomes.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".