Arthroscopic outside-in meniscal repair: A short-term clinical experience
Bibliographic record
Abstract
Objective: Meniscal injuries are very common knee injuries that are presented to an orthopaedic surgeon. The goal of our study was to assess the early outcome of outside-in meniscal repair in the management of meniscal tears. Patients and Methods: This study was a prospective case series conducted at the National Orthopaedic Hospital, Lagos. Consecutive cases of patients with meniscal tears who met the inclusion criteria were recruited. Anterior cruciate ligament reconstruction was performed with semitendinosus autograft. Meniscal repair was performed arthroscopically by only two surgeons using the outside-in technique with size 2 polydioxanone suture. The Western Ontario and McMaster University Evaluation Tool (WOMET) score was computed during the pre-operative stage and at least 6-months post-operatively as outcome measure. The visual analogue scale (VAS) and WOMET scores in the pre- and post-operative periods were noted. Results: Five patients with injured menisci underwent meniscal repair. The median duration of follow-up was 14 months (range 8–30 months). Using Barret’s criteria, we determined that a clinically healed meniscus was obtained in only 2 (40%) patients. The WOMET score improved from a mean of 46 (±18) to 20 (±10.7) between the pre- and post-operative stages, and the mean VAS score decreased from 4.6 (±0.5) to 2.5 (±1.3). Discussion: The poor health-seeking behaviour in our environment would explain the delayed presentations of our patients. However, it has been established that chronic tears do heal. Outside-in technique remains at the moment our method of choice for meniscal repair because of the challenges we face for equipment and funding of health care in our environment. Trephination of the meniscus was performed to improve the chances of healing. Using Barret’s criteria, we had a healing rate of 40% (2). Conclusions: The outside-in technique remains an option for the treatment of chronic tears with good clinical improvement in the short term.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".