Pain intensity rating training
Bibliographic record
Abstract
Clinical trial participants often require additional instruction to prevent idiosyncratic interpretations regarding completion of patient-reported outcomes. The Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership developed a training system with specific, standardized guidance regarding daily average pain intensity ratings. A 3-week exploratory study among participants with low-back pain, osteoarthritis of the knee or hip, and painful diabetic peripheral neuropathy was conducted, randomly assigning participants to 1 of 3 groups: training with human pain assessment (T+); training with automated pain assessment (T); or no training with automated pain assessment (C). Although most measures of validity and reliability did not reveal significant differences between groups, some benefit was observed in discriminant validity, amount of missing data, and ranking order of least, worst, and average pain intensity ratings for participants in Group T+ compared with the other groups. Prediction of greater reliability in average pain intensity ratings in Group T+ compared with the other groups was not supported, which might indicate that training produces ratings that reflect the reality of temporal pain fluctuations. Results of this novel study suggest the need to test the training system in a prospective analgesic treatment trial.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".