Evidence map›Paper›PMID 39926583›Full record

Reviewnpj women's health2025

Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence.

Camran R Nezhat, Tomiko T Oskotsky, Joshua F Robinson, Susan J Fisher, Angie Tsuei, Binya Liu, Juan C Irwin, Brice Gaudilliere, Marina Sirota, David K Stevenson and 1 more

Abstract readReview
In one paragraph

Review in npj women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

11 authors.

Camran R NezhatCenter for Special Minimally Invasive and Robotic Surgery, Camran Nezhat Institute, Stanford University Medical Center, University of California, San Francisco, Woodside, CA 94061 USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California San Francisco, 490 Illinois St, Floor 2, San Francisco, CA 94158 USA.
Joshua F RobinsonCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, 513 Parnassus Ave, Rm. 1621, San Francisco, CA 94143 USA.
Susan J FisherCenter for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, 35 Medical Center Way, Box 0665, San Francisco, CA 94143 USA.
Angie TsueiCenter for Special Minimally Invasive and Robotic Surgery, Camran Nezhat Institute, Woodside, CA 94061 USA.
Binya LiuDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, 513 Parnassus Avenue Room 1600 HSE, San Francisco, CA 94143 USA.
Juan C IrwinDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, 513 Parnassus Avenue Room 1600 HSE, San Francisco, CA 94143 USA.
Brice GaudilliereDepartment of Anesthesiology, Pain, and Perioperative Medicine, and (courtesy) Pediatrics, Stanford University, 3174 Porter Dr, Palo Alto, CA 94304 USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California San Francisco, 490 Illinois St, Floor 2, San Francisco, CA 94158 USA.
David K StevensonDepartment of Pediatrics, Stanford University, 453 Quarry Rd, Palo Alto, CA 94304 USA.
Linda C GiudiceDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, 513 Parnassus Avenue Room 1600 HSE, San Francisco, CA 94143 USA.

Funding

UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI OPOKU-ANANE, JESSICA · 2021 to 2025
$7.1M
NICHD NIH HHS P01 HD106414
6 · The paper itself

Abstract

Endometriosis is an enigmatic disease whose diagnosis and management are being transformed through innovative surgical, molecular, and computational technologies. Integrating single-cell and other omic disease data with clinical and surgical metadata can identify multiple disease subtypes with translation to novel diagnostics and therapeutics. Herein, we present real-world perspectives on endometriosis and the importance of multidisciplinary collaboration in informing molecular, epidemiologic, and cell-specific data in the clinical and surgical contexts.

Indexed as

DiseasesPhysiologyReproductive disorders

Identifiers

PMID39926583
PMCPMC11802455

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.