The Use of an Intra-Articular Depth Guide in the Measurement of Partial Thickness Rotator Cuff Tears
Bibliographic record
Abstract
Purpose. The purpose of this study was to compare the accuracy of the conventional method for determining the percentage of partial thickness rotator cuff tears to a method using an intra-articular depth guide. The clinical utility of the intra-articular depth guide was also examined. Methods. Partial rotator cuff tears were created in cadaveric shoulders. Exposed footprint, total tendon thickness, and percentage of tendon thickness torn were determined using both techniques. The results from the conventional and intra-articular depth guide methods were correlated with the true anatomic measurements. Thirty-two patients were evaluated in the clinical study. Results. Estimates of total tendon thickness (r = 0.41, P = 0.31) or percentage of thickness tears (r = 0.67, P = 0.07) using the conventional method did not correlate well with true tendon thickness. Using the intra-articular depth guide, estimates of exposed footprint (r = 0.92, P = 0.001), total tendon thickness (r = 0.96, P = 0.0001), and percentage of tendon thickness torn (r = 0.88, P = 0.004) correlated with true anatomic measurements. Seven of 32 patients had their treatment plan altered based on the measurements made by the intra-articular depth guide. Conclusions. The intra-articular depth guide appeared to better correlate with true anatomic measurements. It may be useful during the evaluation and development of treatment plans for partial thickness articular surface rotator cuff tears.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".