The doubtful benefits of giving the benefit of the doubt: Lenient scoring of the spatial orientation items on the mini-Mental Status Exam increases false negative rates
Bibliographic record
Abstract
Lenient scoring of spatial orientation errors (SOE) on the Mini-Mental State Exam (MMSE) is common practice, even though it deviates from standard protocol and may compromise its diagnostic power. This study was designed to empirically evaluate the effect of lenient scoring on the MMSE’s classification accuracy. Participants were 113 community dwelling older adults recruited for a research study, representing a wide range of range of neurological status from cognitively healthy to Alzheimer’s disease. Clinical classification was determined by expert assessors based on multiple sources of clinical evidence. Lenient scoring significantly inflated MMSE total scores (d = .88, large effect), and suppressed failure rates (from 26% to 14%). Standard scoring produced superior overall classification accuracy (75% vs. 67%) over lenient scoring and, more importantly, increased sensitivity from .33 to .53, with minimal loss in specificity (from 1.00 to .95). SOEs are empirical markers of cognitive decline and should not be adjusted based on clinical judgment. Results indicate that diminished sensitivity to cognitive impairment is an unintended consequence of lenient scoring and argue against this practice.
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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 | 0.170 | 0.456 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".