The American Academy of Orthopaedic Surgeons Outcomes Instruments
Bibliographic record
Abstract
BACKGROUND: The collection of population-based normative data is a necessary step in the process of standardization of eleven American Academy of Orthopaedic Surgeons (AAOS) musculoskeletal outcomes measures. These data serve as comparative normative scores with which to assess the effectiveness of treatment regimens in clinical practice settings and to study the clinical outcomes of treatment in musculoskeletal research. METHODS: With use of a panel mail methodology, self-reported data on the eleven AAOS musculoskeletal outcomes measures were collected from the general population of the United States. RESULTS: The overall response rate of 67.4% for the various surveys met study expectations. For the eleven measures, the range of the confidence intervals for the surveys was +/-1.6% to +/-2.3%, exceeding the +/-3% set a priori. With use of the Multitrait/Multi-Item Analysis Program, all of the scales within each of eleven measures exhibited high internal reliability as well as discriminant and convergent validity. Items within each of the scales contributed roughly equal proportions of information to the total scale scores. CONCLUSIONS: All eleven instruments met study expectations for providing reliable and valid normative data for use in clinical and research settings.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".