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Record W2091810105 · doi:10.1159/000368231

Management of Ovarian Cancer in 14th Gestational Week of Pregnancy by Robotic Approach with Preservation of the Fetus

2015· article· en· W2091810105 on OpenAlexaff
Ching-Hui Chen, Li-Hsuan Chiu, Cindy Chan, Weimin Liu

Bibliographic record

VenueGynecologic and Obstetric Investigation · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicinePregnancyOmentectomyOvarian cancerGestationSurgeryDissection (medical)UterusFetusMalignancyCancerObstetricsHysterectomyInternal medicine

Abstract

fetched live from OpenAlex

The objective of this study is to present a rare case of pregnancy complicated with ovarian cancer managed by robotic surgery. A 36-year-old woman suffered from sudden onset of lower abdominal pain during her pregnancy at 14 weeks of gestation. As malignancy was highly suspected, left salpingo-oophorectomy, bilateral pelvic lymph node dissection, and omentectomy were performed by robotic approach. The uterus and fetus were preserved. After surgery, 5 courses of carboplatin and paclitaxel were given, and the patient was delivered by cesarean section at 37 weeks of pregnancy. Follow-up at 18 months showed no signs of cancer recurrence. As there is limited report of pregnancy complicated with ovarian cancer managed by robotic surgery, we provide this rare case and suggest that surgical staging for ovarian malignancy can be safely accomplished by robotic approach at 14 weeks of pregnancy.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.254
Teacher spread0.209 · 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 designCase report
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

Citations9
Published2015
Admission routes1
Has abstractyes

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