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Record W4387260593 · doi:10.1002/uog.27214

EP31.18: Beat the clock: predicting surgical times for excision of endometriosis using preoperative ultrasound – a retrospective study

2023· article· en· W4387260593 on OpenAlexaffabout
J. Tigdi, Mahsa Gholiof, Laure Tessier, Hira Niazi, Mathew Leonardi

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineEndometriosisUltrasoundPredictive valueSurgeryCorrelationGynecologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

The primary objective of this study is to determine the correlation between predicted surgical times using endometriosis ultrasound (US) with actual surgical times. Secondary objectives include determining the: 1) correlation between surgeon estimated surgical time and actual surgical time; 2) average anesthetic preparation times; and 3) predictive factors that may account for actual surgical time. This study was conducted at the Endometriosis Clinic at McMaster University in Hamilton, Canada. Patients were included if they underwent laparoscopic excision of endometriosis conducted by a single endometriosis surgeon and gynecologic sonologist between August 2020 and July 2022. Prediction of operating time was recorded using both 1) routine preoperative endometriosis ultrasound based on estimates of surgical time per disease site and 2) surgeon estimation by standard technique. These estimated times were compared with the actual OR time. Thirty-three patients were included. Mean (SD) US estimated OR time and surgeon estimated time was 113.9 (80.6) min and 184.4 (88.0) min respectively. The mean (SD) actual OR time was 172.4 (111.9) min. The average (SD) anesthetic preparation time was 20.3 (13.2) min. There was a strong significant correlation between US estimated OR time and actual OR time (r = 0.75, P-value<0.001). There was a strong significant correlation between surgeon estimated OR time and actual OR time (r = 0.74, P-value<0.001). Moreover, there was a strong and significant correlation between the number of disease sites detected during surgery and OR time (r= 0.69, P-value<0.001). The prediction of laparoscopic excision of endometriosis surgical time by endometriosis ultrasound is strongly correlated with actual surgical time. Though the number of disease sites at time of surgery was most strongly associated with actual OR time, further studies are needed to determine whether endometriosis disease extent by ultrasound staging can reliably predict OR time.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.348
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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