General and Disease-Specific Use of Outcomes Scores for the Shoulder: A Survey of Aossm, Aana, and Isakos Members
Bibliographic record
Abstract
OBJECTIVE: To report on the knowledge and use of both general and disease-specific shoulder outcomes scores among orthopedic surgeons. METHODS: A 22-question Internet survey was administered to members of the American Orthopaedic Society for Sports Medicine, the Arthroscopy Association of North American, and the International Society of Arthroscopy, Knee Surgery, and Orthopedic Sports Medicine via voluntary e-mail participation. Questions targeted demographic information, preferred surgical management of shoulder conditions, and the preferred use of shoulder outcomes instruments in clinical practice. RESULTS: Excluding defunct and duplicate e-mails among membership societies, a total of 3892 unique e-mails were sent, from which 1129 surveys were returned and were fully completed (29%). The largest number of respondents were in private practice (52%); 21% were in academia; and 26% were in a mix of settings. As for location, 74% practiced in the United States, 10% in Europe, 8% in Mexico/South America, and 6% in Asia. A total of 31% total respondents used scores all or most of the time, and 30% used scores at least some of the time. Respondents felt that the 3 most commonly utilized shoulder scores were the American Shoulder and Elbow Surgeons (ASES) score, the University of California at Los Angeles (UCLA) score, and the Constant score. The majority of respondents (76%) performed all-arthroscopic instability repairs. The ASES and Western Ontario Shoulder Instability Index (WOSI) scores were the most preferred measures to monitor instability patients, whether or not the scores were actually implemented in their practice. Most perform between 10 and 25 superior labrum anterior-posterior repairs per year and preferred the ASES, UCLA, and Constant scores for these repairs; rotator cuff repair preferred outcomes instruments were similar. When asked to choose 1 score for all shoulder conditions, the ASES was the clear favorite. CONCLUSIONS: This study reports the knowledge and utilization of shoulder scores for both general and disease-specific conditions. Most respondents preferred the ASES score for most shoulder conditions; however, other scores, such as the WOSI, the Constant, and the Short-Form (SF)-36/12, were popular. This information offers insight into the current and future use of shoulder outcomes both for general and disease-specific use.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".