Qualitative Analysis of the Clinician Interview-Based Impression of Change (Plus): Methodological Issues and Implications for Clinical Research
Bibliographic record
Abstract
The Clinician Interview-Based Impression of Change, plus carer interview (CIBIC-Plus), is widely used in antidementia drug trials. It comprises Likert scales for disease severity and changes, and written accounts summarizing semistructured interviews evaluating behavior, cognition, and function. Studies using the CIBIC-Plus have focused on the numeric scores to the exclusion of the textual data. Our study explored both sets of data to evaluate whether the CIBIC-Plus written data supported (a) the clinicians' global evaluation of patients' changes during treatment, and (b) the emergence of consistent treatment effects. The global (numeric) scales of change were inconsistently supported by the textual data provided in the CIBIC-Plus. No consistent treatment effects were noted. Methodological problems presently limit the retrospective use of the CIBIC-Plus textual data. Improved standardization of note-taking in the CIBIC-Plus textual data may allow for a better understanding of the typical profiles and clinical importance of changes seen in the course of dementia treatment.
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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 | 0.176 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".