Evidence map›Paper›PMID 41913830›Full record

ArticleCureus2026

Preliminary Experience of Robotic Endometriosis From a Tertiary Care Center in India: The Learning Curve.

Anitha Kunnaiah, Keerthana M

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Anitha KunnaiahGynaecology and Obstetrics, Robotic Sciences, Yashoda Hospitals, Hyderabad, IND.
Keerthana MGynaecology and Obstetrics, Robotic Sciences, Yashoda Hospitals, Hyderabad, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Endometriosis often requires surgery, especially in deep-infiltrating cases unresponsive to medical therapy. This study presents our institutional experience with robotic-assisted surgery (RAS) for endometriosis in Indian patients. Methodology This retrospective study was conducted at a high-volume tertiary care center in India and included patients who underwent robotic-assisted endometriosis surgeries using the da Vinci Xi system between October 2023 and April 2025. Results This study analyzed 108 patients, with 39.8% diagnosed with deep infiltrating endometriosis (DIE). The mean age and body mass index of the study groups were 40.9 ± 6.6 years and 26.3 ± 3.3 kg/m², respectively. The average operative time was 129.8 ± 56.0 minutes, with minimal intraoperative and postoperative complications (0.9% each), no conversions to open surgery, no wound infections, and no re-operations. Subgroup analysis revealed that DIE cases had longer operative times. Though complication rates and post-anesthesia care unit stay remained comparable to the non-DIE subgroup. Both subgroups demonstrated significant improvements in postoperative pain and quality of life scores (p < 0.001) compared to their preoperative values. The risk-adjusted cumulative sum analysis of operative and docking times demonstrated distinct learning phases, with marked improvements in surgical efficiency and operating room workflow over time. Notably, a marked inflection point was observed around case 28 in the DIE subgroup. While this subgroup may represent a particularly suitable cohort for the application of robotic techniques, these findings should be interpreted as reflective of procedural optimization within a carefully selected patient population rather than as a definitive statement of superiority. Conclusions This study confirms the feasibility, safety, and effectiveness of RAS for endometriosis in India, with significant postoperative improvements and low complication rates across all stages, including complex DIE cases.

Indexed as

deep infiltrating endometriosisendometriosislearning curvepain scoresquality of life scores (ehp-30)robotic-assisted surgery

Identifiers

PMID41913830
PMCPMC13033049

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