Optimizing the use of patient data to improve outcomes for patients: narcotics for chronic noncancer pain
Bibliographic record
Abstract
Randomized trials can provide important direction to clinical decision-making; however, their strength of inferences may be weakened by methodological limitations, the extent that their reported outcomes fail to address patient-important end points and by failing to report results that provide interpretable estimates of magnitude of effect. Strategies that investigators can use to address interpretability include reporting mean differences between groups in relation to the minimal important difference and reporting the proportion of patients who benefit from treatment and the associated number needed to treat. These strategies also apply to reporting pooled estimates from meta-analyses, even when studies use different instruments to measure the same construct. We illustrate these techniques using, as an example, current evidence for the use of opioids in chronic noncancer pain.
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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 | 0.422 | 0.628 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".