Experimental Execution of the Simulated Pivot‐Shift Test: A Systematic Review of Techniques
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review to identify and summarize the various techniques that have been used to simulate the pivot-shift test in vitro. METHODS: Medline, Embase, and the Cochrane Library were screened for studies involving the simulated pivot-shift test in human cadaveric knees published between 1946 and May 2014. Study parameters including sample size, study location, simulated pivot-shift technique, loads applied, knee flexion angles at which simulated pivot shift was tested, and kinematic evaluation tools were extracted and analyzed. RESULTS: Forty-eight studies reporting simulated pivot-shift testing on 627 cadaveric knees fulfilled the criteria. Reviewer inter-rater agreement for study selection showed a κ score of 0.960 (full-text review). Twenty-seven studies described the use of internal rotation torque, with a mean of 5.3 Nm (range, 1 to 18 Nm). Forty-seven studies described the use of valgus torque, with a mean of 8.8 Nm (range, 1 to 25 Nm). Four studies described the use of iliotibial tract tension, ranging from 10 to 88 N. Regarding static simulated pivot-shift test techniques, 100% of the studies performed testing at 30° of knee flexion, and the most tested range of motion in the continuous tests was 0° to 90°. Anterior tibial translation was the most analyzed parameter during the simulated pivot-shift test, being used in 45 studies. In 22% of the studies, a robotic system was used to simulate the pivot-shift test. Robotic systems were shown to have better control of the loading system and higher tracking system accuracy. CONCLUSIONS: This study provides a reference for investigators who desire to apply simulated pivot shift in their in vitro studies. It is recommended to simulate the pivot-shift test using a 10-Nm valgus torque and 5-Nm internal rotation torque. Knee flexion of 30° is mandatory for testing. LEVEL OF EVIDENCE: Level IV, systematic review of basic science studies.
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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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".