Outcome Measurement in Pharmacological Trials: Validity of the Routine Assessment of Patient Progress (RAPP)
Bibliographic record
Abstract
The assessment of outcomes after treatment with antipsychotic medication is fundamental to clinical care and research. The Routine Assessment of Patient Progress (RAPP) is a reliable multidimensional scale that employs nurses' ratings of symptoms and functioning in psychiatric inpatients. The present study sought to extend validity evidence for the RAPP by examining its ability to reflect changes associated with treatment by antipsychotic medications. The use of a different sample in this study also provided the opportunity to replicate earlier validity data collected on the original set of patients. Ninety-seven separate trials were conducted, involving 65 consecutive admissions to a unit that specializes in the assessment and treatment of patients with long standing severe psychiatric disorders. The RAPP, along with the Positive and Negative Syndrome Scale and global measures of severity, were administered at baseline and at the end of each trial. Both factor scores and clinically-derived subscales were analysed for sensitivity to change. Patients were globally rated as improved, unchanged or worsened at the end of the medication trial. Results indicated that the RAPP factor, clinical scale and total scores compared favourably to other outcome measures in patients rated as improved or worse. In patients rated as unchanged, RAPP scores displayed significantly less change than did the PANSS scores. These findings support the validity of the RAPP as an outcome measure in treatment trials.
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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 | 0.426 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".