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Record W4291167720 · doi:10.1177/23337214221119057

Evaluation of PROMIS Cognitive Function Scores and Correlates in a Clinical Sample of Older Adults

2022· article· en· W4291167720 on OpenAlexaboutno aff
Maria Orlando Edelen, Jordan M. Harrison, Anthony Rodriguez, Rebecca Weir, Jin‐Shei Lai, Michelle M. Langer, Janel Hanmer

Bibliographic record

VenueGerontology and Geriatric Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsCognitionLogistic regressionMedicineMontreal Cognitive AssessmentObservational studyDepression (economics)Physical therapyPatient-Reported Outcomes Measurement Information SystemMedical diagnosisClinical psychologyGerontologyCognitive impairmentInternal medicinePsychiatryPsychometricsComputerized adaptive testingPathology

Abstract

fetched live from OpenAlex

In this study we assessed the utility of self-reported cognitive function using two PROMIS ® Cognitive Function (PROMIS-CF) items in an observational clinical sample of patients aged 65 and older ( n = 16,249) at a large health system. We evaluated the association of PROMIS-CF scores with clinical characteristics and Montreal Cognitive Assessment (MoCA) scores, and we used logistic regression to examine predictors of 1-year decline in PROMIS-CF scores among patients with available data. PROMIS-CF scores were associated with clinical characteristics as hypothesized, with lower (more impaired) scores for patients with cognitive impairment (CI) diagnoses, multiple comorbidities, and those taking cognitive enhancing or interfering medications. PROMIS-CF scores were also positively associated with MoCA scores. Predictors of 1-year decline in PROMIS-CF scores included CI diagnoses, use of cognitive enhancing medications, higher depression scores, and lower social role function. Our findings suggest potential utility of PROMIS-CF items in a brief patient-administered screening tool for CI.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.383
Teacher spread0.333 · 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; a candidate call from one teacher head, not a consensus.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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