Evaluation of PROMIS Cognitive Function Scores and Correlates in a Clinical Sample of Older Adults
Bibliographic record
Abstract
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".