Identifying the optimal treatment among common non-surgical neck pain treatments
Bibliographic record
Abstract
Background. In Canada and the United States, the most commonly prescribed neck pain treatments are standard nonsteroidal anti-inflammatory drugs (sNSAIDs), Cox-2 NSAIDs, exercise, mobilization, and manipulation. Deciding which treatment is best is difficult because of the trade-offs between beneficial and harmful effects, and because of the uncertainty of these effects. Objective. Our objective was to identify the optimal treatment for non-specific neck pain using decision analysis. Methods. (Quality-adjusted) life expectancy associated with standard NSAIDs, Cox-2 NSAIDs, exercise, mobilization, and manipulation were compared in a decision-analytic model. Estimates of the course of neck pain, background risk of adverse events in the general population, treatment effectiveness and risk, and patient-preferences were inputted into the model. Assuming equal effectiveness, we conducted a baseline analysis using risk of harm only. We assessed the stability of the baseline results by conducting a second analysis that incorporated effectiveness data from a high-quality randomized trial. Results. There were no important differences across treatments. The difference between the highest and lowest-ranked treatments predicted by the baseline model was 4.5 days of life expectancy and 3.4 quality-adjusted life-days. The difference between the highest and lowest-ranked treatments predicted by the second model was 7.3 quality-adjusted life-days. Conclusion. When the objective is to maximize life expectancy and quality-adjusted life expectancy, none of the treatments in our analysis were clearly superior.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".