Abstract No.: ABS3618: Implication Of Pre Operative Frailty In Risk Assessment Of Post Operative DeliriumIn Elderly Indian Patients Undergoing Non-Cardiac Surgery
Bibliographic record
Abstract
JOURNAL/ijana/04.03/01762628-202203001-00020/inline-graphic1/v/2022-09-30T091728Z/r/image-tiff JOURNAL/ijana/04.03/01762628-202203001-00020/inline-graphic2/v/2022-09-30T091728Z/r/image-tiff Background & Aims: Frailty is defined as result of a progressive decline in homeostatic capacity and is a vicious cycle. Delirium is defined as acute in onset, inattention, with cognition dysfunction and behaviour abnormalities Methods: Consenting patients above 65 years of age, undergoing non cardiac surgery were enrolled. Frailty was assessed preoperatively with Modified Fried Criteria (MFC) and patients were divided into Frail (MFC<3/7) and Non frail category (MFC>3/7). Postoperatively patients were assessed for delirium by Confusion assessment method , Montreal Cognitive Assessment and 12 item Mini-Mental State Examination on Day 1, Day 3, and telephonically by T-MOCA at 1 and 3 months postoperative Results: 109 patients were enrolled. Post-operative delirium on Day 1 was seen in 9 (8.25%) patients and all patients belonged to frail category. Cognitive dysfunction assessed by MOCA and MMSE on Day1 was seen in 85.7%(81/98) and 63.3% (62/98) in frail elderly significantly higher than 45.5% (5/11), and 27.3% (3/11) in non-frail elderlies. significantly greater cognitive dysfunction was seen in frail patients at Day3, at one month and at three months (77.6% in frail versus 36.4% in non-frail) too. Conclusion: In our study we found that pre-operative Frail elderly patients had significantly increased post-operative cognitive dysfunction at day 1, 3 and 1st month and 3rd month after non-cardiac surgery. Incidence of postoperative delirium however was similar
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".