Effects of prior anterior cruciate ligament reconstruction on clinical outcomes associated with total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: The argument on the clinical effects of previous anterior cruciate ligament (ACL) reconstruction on total knee arthroplasty (TKA) remains to be resolved. The aim of the current study was to compare operative and postoperative outcomes of patients undergoing TKA after ACL reconstruction with a matched cohort of control subjects having primary osteoarthritis and no history of ligament reconstruction. METHODS: This study was performed and reported in accordance with the Strengthening the Reporting of Observational studies in Epidemiology checklist. The institutional review board approval of our hospital was obtained for the study. The ACL and control groups were matched 1:1 using a caliper width of 0.1 for the propensity score through nearest neighbor matching. Written informed consent was obtained from all subjects participating in the trial. The primary outcome measure was postoperative complications. Secondary outcome measures included operative time, tourniquet time, intraoperative complications, Oxford Knee Score, range of motion, and Western Ontario and McMaster Universities index. RESULTS: This study had limited inclusion and exclusion criteria and a well-controlled intervention. We hypothesized that prior ACL reconstruction had a negative impact on the operative and postoperative outcomes of TKA. TRIAL REGISTRATION: This study protocol was registered in Research Registry (researchregistry5598).
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".