IMPACT OF FRAILTY ON THE OCCURRENCE OF DELIRIUM IN THE POSTOPERATIVE CARDIAC SURGERY PATIENT
Bibliographic record
Abstract
There is a lack of information on the interaction of frailty and the occurrence of delirium after cardiac surgery. Specifically, it is unclear if the addition of preoperative frailty screening to existing surgical perioperative risk models improves the prediction of postoperative delirium (PoD). In a prospective observational study, preoperative assessments of frailty (Modified Fried Criteria, the Short Physical Performance Battery and a 35-item Frailty Index) was performed in elective cardiac surgery patients. The primary outcome was PoD, assessed using the Confusion Assessment Method. Seventy-two (54.1%) of the 133 participants were deemed frail. After adjusting for the “traditional” preoperative risk score (EuroSCORE II), frail patients were at increased risk of PoD ([OR], 5.05, 95%CI, 1.58–16.13). The inclusion of a formal assessment of frailty significantly improved the discrimination of the EuroSCORE II in predicting PoD, pointing to opportunities for improved prevention and management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".