Use of the CDR® plus NACC FTLD in mild FTLD: Data from the ARTFL/LEFFTDS consortium
Bibliographic record
Abstract
INTRODUCTION: Behavior/Comportment/Personality (BEHAV) and Language (LANG) domains were added to the Clinical Dementia Rating (CDR®) for improving evaluation of patients with frontotemporal lobar degeneration (FTLD) (CDR® plus NACC FTLD). METHODS: We analyzed the CDR® plus NACC FTLD among participants from the baseline visit of the Advancing Research and Treatment for Frontotemporal Lobar Degeneration/Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects Consortium. RESULTS: The CDR® plus NACC FTLD was able to detect early symptoms in the mildly impaired participants who were rated as CDR® sum of boxes (CDR®-SB) = 0. The CDR®-SB was not sensitive, particularly in participants with mild nonfluent/agrammatic primary progressive aphasia. Participants with familial and sporadic behavioral variant FTD exhibited similar CDR® plus NACC FTLD profiles except that language impairment was more frequent in participants with mild sporadic behavioral variant FTD. Adding the BEHAV and/or LANG domains to the CDR®-SB significantly enhanced discriminatory power in differentiating among the FTLD spectrum disorders. DISCUSSION: The BEHAV and LANG domains enable the CDR® plus NACC FTLD to capture early symptomatology of FTLD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".