Evaluating cognitive screening instruments with the “likelihood to be diagnosed or misdiagnosed” measure
Bibliographic record
Abstract
OBJECTIVES: To calculate "number needed to diagnose" (NND), "number needed to predict" (NNP), and "number needed to misdiagnose" (NNM) for cognitive screening instruments which are commonly used in suspected dementia and mild cognitive impairment, and from these to calculate a "likelihood to be diagnosed or misdiagnosed" (LDM) metric as the ratio of NNM to either NND or NNP. METHODS: Datasets from pragmatic diagnostic test accuracy studies examining four commonly used cognitive screening instruments (Mini-Mental State Examination, MMSE; Montreal Cognitive Assessment, MoCA; Mini-Addenbrooke's Cognitive Examination, MACE; Six-item Cognitive Impairment Test, 6CIT) were analysed to calculate NND, NNP, and NNM, and from these derive values for LDM. FINDINGS: Although all the tests had low NND and NNP as desired, NNM was also low. Hence, only MMSE and 6CIT achieved LDM > 1 for dementia diagnosis, and only MACE and 6CIT had LDM > 1 for diagnosis of mild cognitive impairment. CONCLUSIONS: The likelihood to be diagnosed or misdiagnosed (LDM) metric may indicate the utility or inutility of diagnostic tests for clinicians and patients. LDM values may clarify the inevitable trade-off between sensitivity and specificity and hence clinician purpose in administering the diagnostic test (minimising false negatives or false positives).
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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 | 0.111 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".