P1‐060: Social impact in the measurement of clinically meaningful change: Findings from the cross‐sectional validation of the Clinical Meaningfulness in Alzheimer Disease Treatment (CLIMAT) scale
Bibliographic record
Abstract
There is a need for novel assessment methods in the determination of clinically meaningful change in Alzheimer Disease (AD). The Clinical Meaningfulness in Alzheimer Disease Treatment (CLIMAT) scale is a newly developed instrument that targets two constructs: severity, defined as the magnitude of AD symptoms, and social impact, defined as the importance patients and caregivers attribute to AD symptoms. If social impact can be established as a construct distinct from disease severity, it could aid in weighting treatment benefit in AD. This cross-sectional study investigated the relationship between CLIMAT severity and social impact ratings. The CLIMAT covers social, functional, cognitive and behavioral items in separate patient and informant interviews. In the patient interview, items were rated for severity and impact on self (I-Pat-self). In the informant interview items were rated for severity, impact on patient reported by informant (I-Inf-Pat) and impact on self reported by informant (I-Inf-self). Domain and total scores were computed for all ratings. Pearson correlation coefficients were used to assess the relation between severity and impact ratings. Collinearity was defined as r>.70. Participants were n=23 community-dwelling ‘probable’ AD subjects (MMSE M=19.9, SD=7.3, range 11-28), with spousal informants. For patient ratings, the correlation between total severity and total I-Pat-self ratings was r=.54. Correlations between domain severity and domain I-Pat-self ratings were r<.50 for the social, functional and cognitive, r=.74 for the behavioral domain. For informant ratings, the correlation between total severity and total I-Inf-Pat was r=.50, and correlations between domain ratings r<.50 for social, functional and cognitive, r=.72 for the behavioral domain. The correlation between total severity and I-Inf-self was r=.78, with all correlations between domain ratings r>.70. Disease severity and social impact were hypothesized as two distinct constructs in AD symptom assessment. CLIMAT data largely support this hypothesis. The determination of the social impact on patients appears to add a valid dimension in the assessment of social, functional and cognitive symptoms, and in turn hold promise in the measurement of clinically meaningful response to treatment. The overlap between severity and social impact in the behavioral domain warrants further study.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".