Diagnostic Accuracy and Confidence in the Clinical Detection of Cognitive Impairment in Early-Stage Parkinson Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Mild cognitive impairment (MCI) is present in up to 34% of patients with early-stage Parkinson disease (PD); however, it is difficult to detect subtle impairment without objective cognitive testing. METHODS: Data were obtained from the Parkinson Progression Marker Initiative. All 341 participants were administered the Montreal Cognitive Assessment (MoCA) and a brief neuropsychological battery. Participants were classified as PD-MCI if MoCA was <26 or if they scored ≥1 standard deviation below the normative mean in 2 or more domains, based upon established criteria. The sensitivity/specificity for the clinical detection of PD-MCI was determined. RESULTS: Overall accuracy for clinical detection of PD-MCI was 67.4%. Although clinical determination was highly specific (96.3%; 95% confidence interval [CI]: 0.92-0.98), sensitivity was poor (32.0%; 95% CI: 0.25-0.40). CONCLUSION: Identifying MCI in early-stage PD based on clinical interview alone appears to be insufficient. The inclusion of objective cognitive tests allowing for normative sample comparisons is needed to increase the detection of cognitive impairment in this population.
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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 | 0.023 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".