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Relationship of MRI tests and referral of malignant adnexal masses to gynecologic oncologists for surgery.

2012· article· en· W3011437021 on OpenAlexaff
Rachel Kupets, Gennady Miroshnichenko, Lawrence Paszat

Bibliographic record

VenueJournal of Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAdnexal DiseasesReferralRadiologyLaparotomyAdnexal massMagnetic resonance imagingGynecologic oncologyLogistic regressionWork-upObstetrics and gynaecologyUltrasoundSurgeryLaparoscopyPregnancyInternal medicine

Abstract

fetched live from OpenAlex

6134 Background: To evaluate the patterns of radiologic imaging by family physicians and gynecologists in the work up of women found to have an adnexal mass on pelvic ultrasound. To evaluate whether advanced imaging tests are associated with improved referral of high risk adnexal masses to gynecologic oncologists. Methods: Centralized provincial databases of healthcare utilization were used to identify women aged 45 and older who received a pelvic ultrasound between 2006-2008. Subsequent imaging tests ordered by physician specialty were identified. Of those women who proceeded to laparotomy, logistic regression was performed to determine which imaging tests were associated with referral of high risk adnexal tumors to a gynecologic oncologist. Results: 193, 261 women had a pelvic ultrasound; 19, 949 (10.3%) had a laparotomy. 2223 and 627 women were categorized with benign and malignant adnexal masses respectively. Up to 12% of women had a pelvic MRI and 58% of women had a CT scan after a pelvic ultrasound.Family physicians referred 58% and gynecologists referred 47% of high risk ovarian masses to a gynecologic oncologist respectively after imaging.Gynecologic Oncologists operated on only 55% of women with malignant adnexal masses. On multivariate analysis factors significant for surgery by a gynecologic oncologist include a preoperative CT Scan OR 3.58 (p<.001) and CT Scan and MRI OR 7.78 (p<.001). Preoperative MRI alone had an OR of 1.86 (p=0.09) and was not significant. Mean time to surgery significantly increased when further imaging tests were performed after a pelvic ultrasound (100 days), CT (131 days), MRI (170 days), CT and MRI (179 days),P 0.002. Conclusions: The addition of a pelvic MRI to a pelvic ultrasound does not improve the referral of high risk adnexal masses to a gynecologic oncologist. A consensus on appropriate imaging and triage is needed when an adnexal mass is identified on ultrasound.

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)0.000
metaresearch head score (Gemma)0.010
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.394
GPT teacher head0.526
Teacher spread0.132 · 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
Published2012
Admission routes1
Has abstractyes

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