Author Correction: High inter-rater reliability in consensus diagnoses and overall assessment in the Asian Cohort for Alzheimer’s Disease Study
Bibliographic record
Abstract
In the original version of this article, authors Helena C. Chui and Tiffany W. Chow were listed only under “The Clinical Core of the Asian Cohort for Alzheimer’s Disease (ACAD)” consortium; they have now also been included in the main author group. Authors Gyungah R. Jun and Li-San Wang, previously listed only under “The Clinical Core of the Asian Cohort for Alzheimer’s Disease (ACAD)” consortium, have been moved to “The Asian Cohort for Alzheimer’s Disease Study” consortium and are also included in the main author group. Authors Yun-Beom Choi, Victor W. Henderson, Haeok Lee, Walter A. Kukull, Dolly Reyes-Dumeyer, and Clara Li, originally listed only in the main author group, are now also included in “The Clinical Core of the Asian Cohort for Alzheimer’s Disease (ACAD)” consortium. Author Pei-Chuan Ho has been newly added under “The Clinical Core of the Asian Cohort for Alzheimer’s Disease (ACAD)” consortium. Author Van M. Ta Park, who was previously listed in the main author group and in both consortia, is now listed only in the main author group and under “The Asian Cohort for Alzheimer’s Disease Study” consortium.
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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 | 0.033 | 0.410 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.023 |
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".