Posterior tibial translation resulting from the posterior drawer manoeuver in cadaveric knee specimens: a systematic review
Bibliographic record
Abstract
PURPOSE: The purpose of this systematic review of cadaver-based biomechanical studies is to accurately quantify how much posterior tibial translation occurs during posterior drawer testing in normal and PCL-deficient knees. METHODS: A search of the electronic databases, MEDLINE and EMBASE, was performed to identify relevant cadaveric studies that reported posterior tibial translation during posterior drawer testing. Studies were combined to determine overall increase in posterior tibial translation after PCL sectioning at 90° of flexion. Methodological quality of included studies was assessed by two reviewers using a novel clinometric tool. An intraclass correlation coefficient with 95 % confidence intervals (CIs) was used to determine agreement between reviewers on quality scores. RESULTS: Combined analysis of 244 cadaveric specimens from 23 studies in which the PCL was sectioned yielded a mean net increase in tibial translation of 10.7 mm (95 % CI 9.68-11.8) with posterior drawer testing. Posterior tibial translation among cadaveric specimens with no disruption to any ligamentous structures was found to be 5.4 mm (95 % CI 4.3-6.6). CONCLUSIONS: Cadaveric data support previous study findings of >8 mm of posterior tibial translation on stress radiographs being indicative of isolated PCL insufficiency. Use of fixed reference points and strict control of tibial rotation are imperative to ensure accurate results in cadaveric studies and in the clinical setting when performing the posterior drawer examination. LEVEL OF EVIDENCE: III.
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".