Outcome Assessments in the Evaluation of Treatment of Spinal Disorders
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
Clinicians and researchers increasingly recognize the importance of the patient's perspective in the evaluations of the effectiveness of treatment. The rapid growth in the number and types of patient-based outcome measures can be confusing. This supplement provides a state-of-the-art review of the available tools. In this paper, the key recommendations from the participating authors are summarized. A core set of measures should include the following five domains: back specific function, generic health status, pain, work disability, and patient satisfaction. Two commonly used measures of back-specific function are recommended: the Roland-Morris Disability Questionnaire and the Oswestry Disability Index. Among the generic measures, the SF-36 strikes the best balance between length, reliability, validity, responsiveness, and experience in large populations of patients with back pain. Moreover, the SF-36 Bodily Pain Scale provides a brief measure of pain intensity and pain interference with activities. Health-related work disability should include at a minimum a measure of work status and work-time loss. For those who are still at work, new measures are being developed to measure health-related work limitations. No single measure of patient satisfaction is clearly preferred but guiding principles are provided to choose among available measures. In addition to the five recommended domains, preference-based health outcome measures, including patients utilities, may be useful when there is a need to value alternative health outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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