Excellent healing rates and patient satisfaction after arthroscopic repair of medium to large rotator cuff tears with a single-row technique augmented with bone marrow vents
Bibliographic record
Abstract
PURPOSE: This study evaluated the repair integrity and patient clinical outcomes following arthroscopic rotator cuff repair of medium to large rotator cuff tears using a single-row technique consisting of medially based, triple-loaded anchors augmented with bone marrow vents in the rotator cuff footprint lateral to the repair. METHODS: This is a retrospective study of 52 patients (53 shoulders) comprising 36 males and 16 females with a median age of 62 (range 44-82) with more than 24-month follow-up, tears between 2 and 4 cm in the anterior-posterior dimension and utilizing triple-loaded anchors. Mann-Whitney test compared Western Ontario Rotator Cuff (WORC) outcome scores between patients with healed and re-torn cuff repairs. Multivariate logistic regression analysed association of variables with healing status and WORC score. Cuff integrity was assessed on MRI, read by a musculoskeletal fellowship-trained radiologist. RESULTS: Magnetic resonance imaging (MRI) demonstrated an intact repair in 48 of 53 shoulders (91%). The overall median WORC score was 95.7 (range 27.6-100.0). A significant difference in WORC scores were seen between patients with healed repairs 96.7 (range 56.7-100.0) compared with a re-tear 64.6 (27.6-73.8), p < 0.00056. CONCLUSIONS: Arthroscopic repair of medium to large rotator cuff tears using a triple-loaded single-row repair augmented with bone marrow vents resulted in a 91% healing rate by MRI and excellent patient reported clinical outcomes comparable to similar reported results in the literature. LEVEL OF EVIDENCE: IV.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".