Utilizing Telemedicine for Delivery of Postoperative Care Following Minimally Invasive Gynecologic Surgery
Bibliographic record
Abstract
Objectives: Determine if patient satisfaction is greater after delivering postoperative care via telemedicine following minimally invasive gynecologic surgery. Materials and Methods: University-based outpatient clinic; Randomized controlled trial (Canadian Task Force classification I). Females between 18 and 60 years of age scheduled to undergo laparoscopic hysterectomy or laparoscopic excision of endometriosis were invited to participate. Eligible patients were randomized to receive postoperative care either through a traditional office visit or via telemedicine. PSQ-18 satisfaction surveys were performed by phone after the visit. Results: Forty-one patients were analyzed out of which 25 were in the office group and 16 in the telemedicine group. Groups were homogenous to age (41.4 vs. 43.3 p. 48), body mass index (31.9 vs. 30.6 P = 0.52), distance in miles from home (12.7 vs. 12.4 P = 0.92), and parity ( P = 0.51). PSQ-18 questionnaire was scored and each category was compared between the office and telemedicine groups. When comparing medians (interquartile range), the general satisfaction and time spent with doctor categories were significantly higher in the telemedicine group (4.0 [4.0, 4.5] vs. 4.5 [4.5, 5.0] P = 0.05), (4.0 [4.0, 4.5] vs. 4.5 [4.0, 5.0] P = 0.05). The remainder of the categories analyzed were not different between groups Technical Quality (4.0 [3.8, 4.5] vs. 4.5 [3.9, 5.0] P = 0.13), Interpersonal Manner (4.0 [4.0, 4.5] vs. 4.5 [4.0, 5.0] P = 0.34), Communication (4.5 [4.0, 4.5] vs. 4.5 [4.3, 5.0] P = 0.21), Accessibility and Convenience (4.0 [3.5, 4.5] v 4.0 [3.6, 4.5] P = 0.84). A chart review was performed, examining the first 30 days after surgery. One (4%) patient in the office group went to the ER after postoperative visit, and 0 in the telemedicine group ( P = 0.42). Conclusion: Postoperative care via telemedicine after gynecologic surgery results in higher patient satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".