Head‐to‐head comparisons of cognitive screening tests administered in primary care centers
Bibliographic record
Abstract
Abstract Background Cognitive screening tests are often used in primary care to aid in the decision whether further investigation is needed. The most widely used tests, MMSE and the Clock Drawing Test (CDT), were developed more than 40 years ago and there is a need to update the primary care neuropsychological toolbox. The objective for this pilot study was to examine the diagnostic accuracy of AD for different cognitive tests administered at primary care centers. Method The BioFINDER Primary Care Study consecutively recruits patients with cognitive complaints (SCD, MCI, or mild dementia) at different primary care centers in Sweden. Cognitive tests are examined as well as plasma AD‐biomarkers. A test battery including MMSE, MoCA, CDT, Verbal fluency tests, TMT A and B, Symbol Digit Test, ADAS 10‐wordlist and Recall of Picture Test (RPT) are administered to all patients at their respective primary care center. Patients are then referred to a memory clinic where they undergo a full clinical work‐up (including neuropsychological assessment, lumbar puncture or Aβ‐PET, MRI and an examination by a physician specialized in dementia diseases) after which a diagnosis is made (blinded to the tests done in primary care). A clinical diagnosis of AD is supported by CSF or PET findings. The diagnosis of AD, either at the MCI or mild dementia stage, was here used as the dependent variable in logistic regression models with the different cognitive tests as predictors (adjusted for age, sex, education). Comparisons of AUC were performed using DeLong statistics. Result In this pilot study the first 276 recruited patients were analyzed (Table 1). Four tests, MMSE (AUC = 0.73, P <0.01), MoCA (AUC = 0.71, P <0.05), ADAS 10‐wordlist (AUC = 0.80, P <0.001) and RPT (AUC = 0.79, P <0.001), performed significantly better than the basic model (age, sex, education) in predicting a diagnosis of AD. Both the ADAS 10‐wordlist and RPT performed significantly better than MMSE (P <0.05). Adding the CDT to MMSE did not improve the AUC (0.73). Conclusion In a diverse population, as seen at primary care centers, brief tests of memory may provide better accuracy in detecting AD than standard of care tests as MMSE and MoCA.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".