Decision-making for Postoperative Care in Geriatric Patients Undergoing Minor Surgeries using Mini Mental State Examination, Barthel Index of Activities of Daily Living and CSHA-Clinical Frailty Scale
Bibliographic record
Abstract
Purpose: The prediction of postoperative outcome following major surgeries in elderly patients requires a decision-making process, which was suggested to be constructed on data pertaining to the cognitive function, functional status and frailty. We aimed to evaluate their predictive value for minor surgeries. Material and Methods: Patients, ≥65 years of age with ASA 1-3, scheduled for elective minor surgeries between January-June 2019 were enrolled. MMSE, Barthel Index (BI) of ADL and CSHA-CFS were used to evaluate the cognitive function, functional status and frailty on admission, respectively. The MMSE cut-off point was 24 and the frailty cut-off point was 4. The relationships of these parameters with postoperative status either as outpatient or inpatient, PACU stay, LOS in hospital and readmission within 30 days were evaluated. Results: Ninety-nine patients were included. The MMSE scores, BIs and CSHA-CFS scores were similar in all groups. The number of inpatients were higher among patients either with MMSE 2 (n=20 (83.3%)) (p=0.023). The patients with ASA>2 had higher probability of length of stay >1 day (p=0.036) and for PACU stay (p=0.042) irrespective of frailty score. There was no correlation with readmission within 30 days. Conclusion: ASA>2 was correlated with inpatient status when associated with MMSE1 day in the elderly after minor surgeries. CSHA-CFS ≥4 was also correlated with inpatient status independently.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".