Targeted scoring criteria reduce variance in global impressions
Bibliographic record
Abstract
OBJECTIVE: This study examined the confounding effect of treatment emergent physical or psychic symptoms on clinical global impression (CGI) ratings in CNS trials and examined the benefit of targeted scoring criteria on clarifying ratings and reducing scoring variance. METHODS: Twenty-four raters participating in an investigator meeting training session scored a series of scripted CGI scenarios that included treatment emergent symptoms. RESULTS: The addition of treatment emergent gastrointestinal (GI) symptoms or anxiety symptoms significantly changed the rating of clinical global improvement and caused a broad CGI-improvement (CGI-I) scoring variance reflecting scoring ambiguity amongst these raters. Re-rating after a presentation of well-defined criteria that addressed these scoring issues narrowed the variance and significantly improved inter-rater reliability. CONCLUSIONS: It is clear that CNS trials must define scoring criteria for global ratings prior to the initiation of a study to assure ratings consistency. The actual definition of global must be study-specific and may depend upon the targeted symptoms of interest and mechanism of drug action. The targeted criteria that define global must be included in all published reports about the 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 | 0.097 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".