Postoperative Cognitive Decline in Patients Undergoing Major Gynecologic Oncology Surgery: A Pilot Prospective Study
Bibliographic record
Abstract
OBJECTIVES: Postoperative cognitive decline (POCD) is characterised by deficits in attention, memory, executive function, and information processing that persist beyond the early postoperative period. Its incidence ranges from 10%-25% after noncardiac surgery. Limited literature exists on POCD after gynecologic oncology surgery. Our primary objective was to identify the incidence of POCD among patients 55 years or older undergoing major gynecologic oncology surgery. METHODS: This mixed-methods, prospective, observational cohort study followed patients 55 years or older who underwent surgery for gynecologic malignancies between February and July 2022. Semi-structured interviews and the Mini-Mental State Exam (MMSE) were administered before surgery as well as 1 and 3 months after. Assessments were delivered virtually and in-person in the context of the COVID-19 pandemic. POCD was defined as ≥2-point decline from baseline MMSE score. RESULTS: Twenty-four patients participated; 19 completed the 1-month follow-up, and 15 completed the 3-month follow-up. The average age was 64 (range: 56-90). The mean preoperative MMSE score was 16.6 out of 17 (virtual) and 12.9 out of 13 (in-person). Two patients had a 1-point decline in their 1-month MMSE score; both recovered by 3 months. One patient had a 1-point decline in their 3-month MMSE score. Semi-structured interviews revealed common themes of "brain fog" at the 1-month follow-up and mild, persistent attention and word-finding deficits at 3 months postoperatively. CONCLUSIONS: This study's qualitative component captured subtle subjective findings suggestive of potential POCD. Larger studies are required, and a more extensive neuropsychological test battery may be required to elicit subtle findings not clearly reflected by MMSE scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".