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Record W4411955792 · doi:10.1016/j.jogc.2025.103025

Feasibility of Same-Day Discharge in Patients Undergoing Laparoscopic Gynaecologic Oncology Surgery

2025· article· en· W4411955792 on OpenAlexaffvenue
Jack Thorburn, Joannie Neveu

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2025
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineGynecologic oncologyLaparoscopic surgeryLaparoscopyGeneral surgeryGynecologic cancerSurgeryInternal medicineCancerOvarian cancer

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the safety and feasibility of same-day discharge (SDD) of oncology patients undergoing complex laparoscopic gynaecologic oncology surgery. METHODS: A retrospective review including patients from October 2019 to July 2023 undergoing surgical staging for endometrial, tubal, or cervical cancer, treatment for endometrial hyperplasia or pelvic masses. Surgeries included a total laparoscopic hysterectomy. Patients accomplishing SDD were compared with those requiring admission. Data collection included clinical, demographic, and perioperative variables up to 6 weeks after surgery. Univariate and multivariate analyses were conducted. RESULTS: (OR 0.207; 95% CI 0.075-0.569, P = 0.002), operative time ≥181 minutes (OR 0.143; 95% CI 0.057-0.361, P < 0.001), and an operative start time after 2:00 PM or later (OR .135; 95% CI 0.036-0.503, P = 0.003). Patient's location <1 hour away from the centre significantly increased the odds of SDD (OR 2.50; 95% CI 1.068-5.863, P = 0.035). Of 51 patients who accomplished SDD, there was a <4% failure rate, with those who were discharged requiring admission >96 hours postoperatively. The average length of stay was 1.09 days. CONCLUSIONS: SDD is safe and feasible for patients. There are few complications, re-admissions, or unscheduled patient contacts postoperatively. Its success can be increased by refining patient selection using predictive variables.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.266
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

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