Preoperative mild cognitive impairment as a risk factor of postoperative cognitive dysfunction in elderly patients undergoing spine surgery
Bibliographic record
Abstract
Introduction: Any persistent degree of cognitive impairment in older adults is a concern as it can progress to dementia. This study aimed to determine the incidence and risk factors for early postoperative cognitive dysfunction (POCD) in elderly patients undergoing spine surgery. Methods: Patients were enrolled from a previous prospective observational study after screening for normal cognitive function using the Mini Mental State Examination (MMSE). Cognitive function was evaluated before surgery and at 1 week, month, and year post-surgery using MMSE and Montreal Cognitive Assessment scores (MoCA). Mild cognitive impairment (MCI) was determined using the MoCA scores adjusted for age. POCD was defined as a drop of three or more points on the MMSE 1 week post-surgery. Multivariate logistic analysis was performed to identify POCD risk factors. Results: < 0.001, respectively). Among them, 10.3% were assessed for POCD on postoperative day 7 and self-reported poor social roles and physical functioning 1 week postoperatively. Conclusion: Preoperative MCI was seen in ~20% of surgical patients aged >70 years. POCD was seen in ~20% of patients with pre-existing MCI, and ~ 10% of those without. Benzodiazepine use, significant comorbidities, pre-existing MCI, and depressive tendencies were risk factors for POCD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".