P4‐095: COMPARISON OF FOUR NEW CONSENSUS CRITERIA AGAINST THE 1984 NINCDS‐ARDRA CRITERIA FOR ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Diagnosis of Alzheimer's disease (AD) has undergone significant revision, largely in response to advances in biomarker research and the understanding of AD's syndromic complexity. The new criteria include: (1) International Working Group (IWG; Dubois 2007 and 2010), (2) International Classification of Disease (ICD-10; WHO 2010), (3) National Institute on Aging - Alzheimer's Association (NIA-AA; McKhann 2011), and (4) DSM-5 (APA 2013). These differ in requirements for memory impairment, functional decline, biomarkers, and allowance for disease subtypes and mixed pathologies (Visser 2012). Comprehensive, systematic comparison in a group of well-characterized AD subjects remains to be done. Clinical history and imaging for 101 individuals from the Sunnybrook Dementia Study who met the 1984 NINCDS-ARDRA criteria (McKhann 1984), for probable AD were reviewed, applying the new criteria. Tc 99 -SPECT was used instead of FDG-PET for the NIA-AA and IWG criteria. The NIA-AA and NINCDS-ARDRA criteria had excellent agreement, with 90% (n=91) of those meeting the original 1984 McKhann criteria also satisfying the revised NIA-AA criteria. Among those that did not, 7 had insufficient functional decline to fulfill dementia criteria, and would be classified as MCI. By contrast, 47% (n=47) of those meeting the 1984 McKhann criteria failed to meet the IWG criteria, similar to the results from a prior study comparing it to the ICD-10/DSMIV(Oksengard 2010). This may reflect the IWG's strict requirement for biomarkers and predominantly amnestic course, and disallowance of co-pathology. Indeed, all atypical individuals from our cohort and those with mild-to-moderate white matter disease were rejected. Similarly, only 44% (n=44) of those meeting the 1984 McKhann criteria met the ICD-10 criteria, likely reflecting construct differences , namely, ICD-10's unique requirements for functional impairment and behavioural symptoms. 85% (n=86) of those meeting the 1984 McKhann also met the DSM-V. Both allow for non-amnestic presentations and are largely based on a similar combination of cognitive and functional factors. Differences in syndrome construct (including functional decline), biomarker use, and allowance for co-pathology within criteria significantly affect the diagnostic classification of individuals with dementia. Going forward, such differences merit careful validation to ensure that criteria accurately and meaningfully reflect disease.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".