Mode of Minimally Invasive Surgery Associated with Venous Thromboembolism Incidence in Gynecologic Cancer Patients
Bibliographic record
Abstract
Postoperative venous thromboembolism (VTE) after minimally invasive surgery (MIS) for gynecologic malignancy is uncommon. Our objective was to characterize the rates and identify risk factors of postoperative VTE. A retrospective cohort study of patients undergoing MIS for gynecologic malignancy at three Canadian institutions from 2014 to 2020 was performed. The primary outcome was incidence of VTE within 90 days post-operatively. Descriptive statistics were used for clinicopathologic factors, and univariate analysis compared differences between groups. Rate and 95% confidence interval for VTE per 1000 surgeries were calculated. A total of 1786 patients met inclusion criteria, 85.3% uterine, 11.5% cervical, and 2.3% had ovarian cancer. Modes of surgery included robotic (49.4%), laparoscopic (20.7%), or combined laparoscopic/vaginal (29.9%). There were 15 VTE events at 90 days post-operatively (0.84%). Rates of VTE were lowest in patients who underwent robotic surgery, followed by combined laparoscopic/vaginal, and highest in a laparoscopic approach (p = 0.047). Pelvic lymphadenectomy (p = 0.038) and adjuvant chemotherapy (p = 0.022) were the only significant factors associated with risk of VTE. The incidence of VTE after MIS for gynecologic malignancy is low. Robotic surgery was associated with a lower incidence, although event rates are low, and further research is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".