Rotator Cuff Tears Are Related to the Side Sleeping Position
Bibliographic record
Abstract
Purpose To determine whether there was a relationship between sleep position and symptomatic partial‐ and full‐thickness rotator cuff tears. Methods A consecutive series of patients that met the inclusion/exclusion criteria (n = 58) were in seen in clinic between July 2019 and December 2019. All of these individuals had a significant partial‐thickness (> 50%) or full‐thickness rotator cuff tear determined by either ultrasound, magnetic resonance imaging, or both. All patients in this series either had an insidious onset of shoulder pain or their symptoms were related to the basic wear and tear of daily activities. Traumatic rotator cuff tears (those associated with a significant traumatic event such as shoulder instability, motor vehicle accidents, sports related injuries, etc.) were excluded. Previous shoulder surgery, recurrent rotator cuff tears, and worker’s compensation cases also were excluded from this series. As part of the history‐taking process, the patients were asked what was their preferred sleeping position—side sleeper, back sleeper, or stomach sleeper. A χ 2 test was conducted to determine the relationship between rotator cuff pathology and sleep position. Results Of the 58 subjects, 52 of the patients were side sleepers, 4 were stomach sleepers, 1 was a back sleeper, and 1 preferred all 3 positions. Statistical analysis, using the χ 2 test ( P < .0001), demonstrated that rotator cuff tears were most often seen in side sleepers. Conclusions In our study, there appeared to be a relationship between the preference of being a side sleeper and the presence of a rotator cuff tear. Level of Evidence Level IV, prognostic case series.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".