Position statement: management of rotator cuff tears in adults
Bibliographic record
Abstract
We sought to compare success and re-tear rates of surgically treated full-thickness tears of the rotator cuff in men and women older than 18 years of age to develop a guideline intended for orthopedic surgeons and other health care providers who assess, counsel and care for these patients. We searched Medline, Embase and Cochrane databases through to Apr. 20, 2021, and included all English-language randomized trials comparing single-row versus double-row fixation via arthroscopic approaches; latissimus dorsi transfer (LDT) versus partial rotator cuff repair, lower trapezius transfer (LTT), and superior capsular reconstruction (SCR); and early versus late arthroscopic rotator cuff repair for traumatic tears. We also considered observational studies comparing LDT with LTT and partial repair and studies comparing early versus late treatment of traumatic rotator cuff tears. Outcomes of interest were functional outcomes, pain outcomes, and re-tear rates associated with these interventions. We rated the quality of the evidence and strength of recommendations using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. This guideline will benefit patients seeking surgical intervention of full thickness rotator cuff tears by improving counselling on surgical treatment options and outcomes. It will also benefit surgical providers by expanding their knowledge of various surgical approaches. Data presented could be used to develop frameworks and tools for shared decision-making.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".