Preoperative Cognitive Screening Predicts Postoperative Ambulatory Recovery: A Pilot Study
Bibliographic record
Abstract
Purpose: Cardiac surgery patients are older adults at risk for postoperative physical impairment. We hypothesize that screening for mild cognitive impairment (MCI) using the Montreal Cognitive Assessment (MoCA) may identify patients at risk for delayed physical recovery. Patients and Methods: This is a single center prospective cohort pilot study of patients undergoing elective cardiac surgery. Patients were screened preoperatively for MCI using the MoCA full version 8.1, with scores < 26 defined as a positive screen for MCI. Daily step count, as a practical measure of physical function, was continuously recorded from the preoperative period (baseline) through postoperative day 50 using a commercially available activity tracker. Results: Eighteen patients met inclusion criteria. Eight (44%) screened positive for MCI and 10 (56%) screened non-impaired (NI). Baseline characteristics and operative outcomes were similar between the MCI and NI cohorts. The MCI cohort did not achieve their preoperative daily step count by postoperative day 50, whereas the NI cohort did so by postoperative day 37. There was a difference in median [IQR] postoperative daily step count (MCI: 3662[2292-5704] vs NI: 4497[2532-9216]; p < 0.001). Conclusion: In a cohort of patients older than 60 undergoing cardiac surgery, a preoperative screen for MCI delayed postoperative physical recovery. A brief preoperative cognitive screen tool may identify patients who could benefit from early intervention for recovery of physical function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".