P3‐017: The Impact of Central Rating Review Programs on ADAS‐COG Error Variance
Bibliographic record
Abstract
Rater administration and scoring errors on the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) may be masking positive drug effects in Alzheimer disease (AD) clinical trials (Kobak, 2010; Schafer et al., 2011). Such errors can contribute to larger error variance, higher sample sizes, and reduced power to detect a treatment effect. Despite rigorous rater training and clinical review programs, raters continue to make at least one error per in-study assessment throughout the life of a clinical trial (Bertzos et al., 2013; Perez et al., 2013). Few studies have examined the impact these errors could have on ADAS-Cog outcome data if left uncorrected. This study examines how ADAS-Cog errors impact overall error variance of the ADAS-Cog total scores. ADAS-Cog data from a clinical trial using 436 mild to moderate AD subjects, randomized to two different treatment cohorts were analyzed. A total of 1940 assessments administered by 117 raters across five study visits were centrally reviewed by clinicians. Errors identified were addressed and corrected by the raters. ADAS-Cog scores were calculated for each visit using both corrected and uncorrected scores. The standard deviations and standard errors of the mean between the corrected and uncorrected ADAS-Cog total scores will be compared at each visit for each treatment cohort. It is hypothesized that the clinical review process will decrease the error variance within each of the five visits as well as across time for each treatment cohort. The impact of central review programs on ADAS-Cog error variance, across the life of a trial, will be discussed to determine whether these programs improve the quality of the data. In addition, the impact that central rating review programs have on estimated sample sizes will be explored.
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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 | 0.352 | 0.685 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".