Outcome Assessments in the Evaluation of Treatment of Spinal Disorders
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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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".