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Record W4391428454 · doi:10.1097/wad.0000000000000609

Compassion Versus Accuracy

2024· article· en· W4391428454 on OpenAlexaff
Katrina J. Kent, Nesrine Adly Ibrahim, Kristoffer Romero, Shannon D. Baker, Matthew Greenacre, Chantal M. Boucher, Robert M. Roth, László A. Erdődi

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of WindsorWestern University
Fundersnot available
KeywordsCognitive impairmentCognitionDementiaNeuropsychologyOperationalizationMini–Mental State ExaminationClinical psychologyPsychologyMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.030
GPT teacher head0.352
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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