Cognitive assessment in a predominantly Hispanic and Native American population in New Mexico and its association with kidney transplant wait‐listing
Bibliographic record
Abstract
The association between cognitive function and the likelihood of kidney transplant (KT) wait-listing, especially in minority populations, has not been clearly delineated. We performed a retrospective review of our pre-KT patients, who consist mainly of Hispanics and Native Americans, over a 16-month period. We collected data on baseline demographics and the Montreal Cognitive Assessment (MoCA) score, at the initial KT evaluation. We defined cognitive impairment as MoCA scores of <24. We constructed linear regression models to identify associations between baseline characteristics with MoCA scores and used Cox proportional hazards models to assess associations between MoCA score and KT wait-listing. During the study period, 154 patients completed the MoCA during their initial evaluation. Mean (standard deviation) MoCA scores were 23.9 (4.6), with 58 (38%) participants scoring <24. Advanced age, lower education and being on dialysis were associated with lower MoCA scores. For every one-point increase in MoCA, the likelihood of being wait-listed increased 1.10-fold (95% CI 1.01-1.19, P = .022). Being Native American and having kidney disease due to diabetes or hypertension were associated with longer time to wait-listing. Cognitive impairment was common in our pre-KT patients and was associated with a lower likelihood of KT wait-listing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".