Device‐assisted tensioning is associated with lower rates of graft failure when compared to manual tensioning in ACL reconstruction
Bibliographic record
Abstract
PURPOSE: To describe (1) the current graft tensioning practices in ACL reconstruction (ACLR) and, (2) the failure rates with the use of manual tensioning, or device-assisted tensioning at the time of graft fixation. METHODS: The electronic databases MEDLINE, EMBASE, and PubMed were searched independently by two reviewers from database inception to search date on January 21, 2017. Inclusion criteria were studies reporting graft tensioning method and rate of graft failure. The definition of graft failure used was: (1) side-to-side instrumented laxity > 5 mm, (2) Lachman 2 +, (3) positive pivot-shift testing, (4) MRI-confirmed graft rupture or, (5) need for revision surgery. RESULTS: A total of 3379 patients and 3380 knees were treated with ACL reconstruction and followed for an average of 41.7 months (range 4-145 months). ACLR with manual tensioning was performed on 1518 (51.9%) patients and device-assisted tensioning was performed on 1802 (48.1%) patients. The average knee position reported was 29.2° in single-bundle ACLR and 22.9° in double-bundle ACLR. The median amount of tension used in manual tensioning was 'maximum manual tension' and 50 N in device-assisted tensioning. Overall, the failure rate in studies reporting manual tensioning was 8.9% compared to 4.3% in device-assisted tensioning. CONCLUSION: Both manual tensioning and device-assisted tensioning are associated with low overall failure rates (< 10%) in ACLR; however, there is a higher rate of reported failure with manual tensioning compared to device-assisted tensioning. These findings highlight the need to investigate variations in graft tensioning practice, such as specific tension devices and their parameters, with high-quality, randomized controlled trials to elucidate details of their clinical impact. LEVEL OF EVIDENCE: Level IV, systematic review of level I-IV studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".