Disparities in Rotator Cuff Tear Progression Definitions and Rates
Bibliographic record
Abstract
Background: While rotator cuff tears are prevalent in the general population, the natural history of this disease is unclear. Understanding rotator cuff tear progression is crucial for refining surgical indications and evaluating the necessity of early interventions. This study presents an in-depth analysis of the existing literature on the definitions and progression rates of rotator cuff tears, aiming to enhance clinical decision making and patient outcomes. Methods: A systematic literature search was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, using Medline (PubMed), Embase (Elsevier), and Web of Science databases on January 12, 2023. Articles were identified as relevant to the natural history and progression of asymptomatic and symptomatic partial-thickness (PT) and full-thickness (FT) rotator cuff tears. Those written in English reporting rotator cuff progression rates of tears in adults, based on magnetic resonance imaging (MRI) or ultrasound, were included. After reviewing the articles, the data on the rates of tear progression and associated risk factors were extracted, compiled, and analyzed. The risk of bias was determined using the Newcastle-Ottawa Scale. Results: Twenty-one articles met the inclusion criteria, with 1,831 tears included. The progression rate for all partial thickness tears was 26.7% ± 12.8% at an average follow-up of 2.2 ± 0.9 years, with 5 definitions for tear progression. For FT tears, the progression rate was 54.9% ± 18.6% at a follow-up time of 3.0 ± 2.0 years, with 8 definitions for tear enlargement. A significant difference (p < 0.0001) was found between the progression rates of PT and FT tears. Patients who were initially asymptomatic and became symptomatic had higher progression rates (33%-63%) than those who remained asymptomatic (4%-38%). Conclusion: Further research would benefit by identifying a clinically relevant and standardized definition of rotator cuff tear progression, to describe the natural history of rotator cuff disease, making results more comparable and optimizing treatment planning. Level of Evidence: Level II. See Instructions for Authors for a complete description of levels of evidence.
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 | 0.040 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".