Leg Alignment in Beach\nChair Position Yielding\nOptimal Outcomes in\nShoulder Surgery\nPatients
Bibliographic record
Abstract
Shoulder surgeries are routine in the orthopedic setting; however, patients report high pain intensity and interference with daily life postoperatively. There are no specific standards for positioning patients in a beach chair position during shoulder surgery, which may influence patient outcomes. Complications due to beach chair position have included neurologic, cerebrovascular and cardiovascular problems in recent studies. This study aims to describe patient outcomes in the beach chair position by comparing frog and straight-legged alignment. Leavell and Clark’s Level of Prevention Theory provides the conceptual framework for the study because it emphasizes the importance of the nurses’ role in primary prevention. The study consists of adult participants undergoing outpatient shoulder surgery. Once recruited and informed consent is obtained, the nurses will administer a McGill Pain Questionnaire in addition to the established preoperative assessment. Subjects will be randomly assigned to a group: frog or straight-legged beach chair. A nurse will repeat the McGill Questionnaire in addition to the standard follow-up assessment one day postoperative. The sample size is to be determined. Research will be conducted at an outpatient surgical center in West Michigan. SPSS statistical software will be used for analysis. ANOVA tests will be used to determine whether frog or straight-legged beach chair has on average a significant difference in pain intensity. Results and conclusions are pending. The study’s small sample size, restrictive criteria for eligible participants and observation under only one surgeon limit the study’s generalizability. It is anticipated that this research will launch future studies examining the nurses’ role in positioning patients to reduce postoperative pain intensity and interference with daily activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".