Preoperative Cognitive Impairment as a Predictor of Postoperative Outcomes in Elderly Patients Undergoing Spinal Surgery for Degenerative Spinal Disease
Bibliographic record
Abstract
Cognitive status has been reported to affect the peri-operative and post-operative outcomes of certain surgical procedures. This prospective study investigated the effect of preoperative cognitive impairment on the postoperative course of elderly patients (n = 122, >65 years), following spine surgery for degenerative spinal disease. Data on demographic characteristics, medical history, and blood analysis results were collected. Preoperative cognition was assessed using the mini-mental state examination, and patients were divided into three groups: normal cognition, mild cognitive impairment, and moderate-to-severe cognitive impairment. Discharge destinations (p = 0.014) and postoperative cardiopulmonary complications (p = 0.037) significantly differed based on the cognitive status. Operation time (p = 0.049), white blood cell count (p = 0.022), platelet count (p = 0.013), the mini-mental state examination score (p = 0.033), and the Beck Depression Inventory score (p = 0.041) were significantly associated with the length of hospital stay. Our investigation demonstrated that improved understanding of preoperative cognitive status may be helpful in surgical decision-making and postoperative care of elderly patients with degenerative spinal disease.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".