The “Portable” CDR
Bibliographic record
Abstract
The Clinical Dementia Rating (CDR) is a common rating system used in clinical trials and longitudinal research projects to rate the presence and severity of cognitive problems in Alzheimer disease and related disorders. The interview process requires training and can be time-consuming. Here, we describe the validity, reliability, and discriminative ability of a computer-generated CDR using a personal digital assistant format. This project used clinical data from 138 archival and live evaluations (patient and informant interviews) collected for research purposes at Washington University to develop and test a software-based system for the administration and automatic scoring of the CDR. The system was programmed for use on a hand-held computer via the Palm Operating System. We developed domain-specific algorithms to quantify and translate clinical scoring decisions for the 3 cognitive (Memory, Orientation, Judgment and Problem Solving) and the 3 functional (Community Affairs, Home and Hobbies, Personal Care) domains of the CDR. An acceptable set of algorithms were developed using data from 104 research cases, reflecting a range of impairment levels (CDR 0 to 3) and expert scoring decisions. These algorithms were then tested for accuracy in a validation sample of 34 cases. The computer-generated CDR has excellent internal consistency (Cronbach's alpha ranging from 0.94 to 0.98) and interrater reliability (intraclass correlation coefficient ranging from 0.88 to 0.96). The computer-generated CDR showed excellent discrimination between demented and nondemented cases (Area under the curve=0.95; 95% confidence interval, 0.84-1.1). The computer-generated CDR using a Palm Operating System is easy to use, valid, and reliable. The level of agreement compares favorably to published interrater reliability data for the CDR. Software-based administration and automatic scoring of the CDR is a viable alternative to paper-based methods and may be useful in research and clinical settings, especially where electronic data management and reliability in scoring are critical.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".