The 2002 NIMH Provisional Diagnostic Criteria for Depression of Alzheimer’s Disease (PDC-dAD): Gauging their Validity over a Decade Later
Bibliographic record
Abstract
Presented herein is evidence for criterion, content, and convergent/discriminant validity of the NIMH-Provisional Diagnostic Criteria for depression of Alzheimer's Disease (PDC-dAD) that were formulated to address depression in Alzheimer's disease (AD). Using meta-analytic and systematic review methods, we examined criterion validity evidence in epidemiological and clinical studies comparing the PDC-dAD to Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV), and International Classification of Disease (ICD 9) depression diagnostic criteria. We estimated prevalence of depression by PDC, DSM, and ICD with an omnibus event rate effect-size. We also examined diagnostic agreement between PDC and DSM. To gauge content validity, we reviewed rates of symptom endorsement for each diagnostic approach. Finally, we examined the PDC's relationship with assessment scales (global cognition, neuropsychiatric, and depression definition) for convergent validity evidence. The aggregate evidence supports the validity of the PDC-dAD. Our findings suggest that depression in AD differs from other depressive disorders including Major Depressive Disorder (MDD) in that dAD is more prevalent, with generally a milder presentation and with unique features not captured by the DSM. Although the PDC are the current standard for diagnosis of depression in AD, we identified the need for their further optimization based on predictive validity evidence.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".