The American Academy of Orthopaedic Surgeons Outcomes Instruments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it