Compassion Versus Accuracy
Bibliographic record
Abstract
The Mini-mental State Examination (MMSE) is a commonly used screening tool for cognitive impairment. Lenient scoring of spatial orientation errors (SOEs) on the MMSE is common and negatively affects its diagnostic utility. We examined the effect of lenient SOE scoring on MMSE classification accuracy in a consecutive case series of 103 older adults (age 60 or above) clinically referred for neuropsychological evaluation. Lenient scoring of SOEs on the MMSE occurred in 53 (51.4%) patients and lowered the sensitivity by 7% to 18%, with variable gains in specificity (0% to 11%) to psychometrically operationalized cognitive impairment. Results are consistent with previous reports that lenient scoring is widespread and attenuates the sensitivity of the MMSE. Given the higher clinical priority of correctly detecting early cognitive decline over specificity, a warning against lenient scoring of SOEs (on the MMSE and other screening tools) during medical education and in clinical practice is warranted.
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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 | 0.058 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".