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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 OpenAlex
Ching-Hui Chen, Li-Hsuan Chiu, Cindy Chan, Weimin Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

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

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