A Prospective Observational Cohort Study of Language Preference and Preoperative Cognitive Screening in Older Adults: Do Language Disparities Exist in Cognitive Screening and Does the Association Between Test Results and Postoperative Delirium Differ Based on Language Preference?
Bibliographic record
Abstract
BACKGROUND: A greater percentage of surgical procedures are being performed each year on patients 65 years of age or older. Concurrently, a growing proportion of patients in English-speaking countries such as the United States, United Kingdom, Australia, and Canada have a language other than English (LOE) preference. We aimed to measure whether patients with LOE underwent cognitive screening at the same rates as their English-speaking counterparts when routine screening was instituted. We also aimed to measure the association between preoperative Mini-Cog and postoperative delirium (POD) in both English-speaking and LOE patients. METHODS: We conducted a single-center, observational cohort study in patients 65 years old or older, scheduled for surgery and evaluated in the preoperative clinic. Cognitive screening of older adults was recommended as an institutional program for all patients 65 and older presenting to the preoperative clinic. We measured program adherence for cognitive screening. We also assessed the association of preoperative impairment on Mini-Cog and POD in both English-speaking and LOE patients, and whether the association differed for the 2 groups. A Mini-Cog score ≤2 was considered impaired. Postoperatively, patients were assessed for POD using the Confusion Assessment Method (CAM) and by systematic chart review. RESULTS: Over a 3-year period (February 2019-January 2022), 2446 patients 65 years old or older were assessed in the preoperative clinic prior. Of those 1956 patients underwent cognitive screening. Eighty-nine percent of English-speaking patients underwent preoperative cognitive screening, compared to 58% of LOE patients. The odds of having a Mini-Cog assessment were 5.6 times higher (95% confidence interval [CI], 4.6-7.0) P < .001 for English-speaking patients compared to LOE patients. In English-speaking patients with a positive Mini-Cog screen, the odds of having postop delirium were 3.5 times higher (95% CI, 2.6-4.8) P < .001 when compared to negative Mini-Cog. In LOE patients, the odds of having postop delirium were 3.9 times higher (95% CI, 2.1-7.3) P < .001 for those with a positive Mini-Cog compared to a negative Mini-Cog. The difference between these 2 odds ratios was not significant ( P = .753). CONCLUSIONS: We observed a disparity in the rates LOE patients were cognitively screened before surgery, despite the Mini-Cog being associated with POD in both English-speaking and LOE patients. Efforts should be made to identify barriers to cognitive screening in limited English-proficient older adults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".