Gynecologic oncology robot‐assisted surgery in octogenarians: Impact of age on hospital stay
Bibliographic record
Abstract
OBJECTIVE: To compare postoperative stay in octogenarians and younger patients undergoing gynecologic oncology robot-assisted surgery. METHODS: A retrospective review of robot-assisted surgery in Gynecological Oncology division during 2019-2022. We included all consecutive cases. Octogenarians (age ≥80 years) and younger patients were investigated by univariable analysis for characteristics and outcome. RESULTS: A total of 816 robot-assisted surgeries were performed, 426 (52.2%) endometrial cancer, 159 (19.5%) ovarian cancer, 27 (3.3%) cervical cancer, 35 (4.3%) endometrial intraepithelial neoplasia, and in 169 (20.7%) the final pathology was benign. There were 60 (7.4%) octogenarians and 756 (92.6%) younger patients. The proportion of patients with an American Society of Anesthesiology score greater than 2 was higher among octogenarians (66.7% vs 32.0%, P < 0.001). The median console time, surgical time, and total operation theater time were similar between groups (P = 0.303, P = 0.643 and P = 0.688, respectively). Conversion rate did not differ between groups (0.4% among younger patients vs 0% in octogenarians, P > 0.99). The median length of stay in the recovery room was similar in both groups (median 170 min, interquartile range [IQR] 125-225 min vs 170 min, IQR 128-240 min in octogenarians, P = 0.731). Length of hospital stay was similar in both age groups; median 1 day (IQR 1-1) among octogenarians versus 1 (0-1) in younger patients (P = 0.136). CONCLUSION: Octogenarians undergoing robotic surgery have no increased risk of length of stay or conversion to laparotomy compared with younger patients.
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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".