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Record W3133406891 · doi:10.1016/j.ijscr.2021.105669

Survival of large volume recurrent endometrial cancer with peritoneal metastases treated by cytoreductive surgery, HIPEC and EPIC. Report of a case

2021· article· en· W3133406891 on OpenAlexaff
Paul H. Sugarbaker

Bibliographic record

VenueInternational Journal of Surgery Case Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsResearch Institute in Oncology and Hematology
Fundersnot available
KeywordsMedicineCytoreductive surgeryEPICEndometrial cancerSurgeryPeritoneal carcinomatosisCancerGeneral surgeryInternal medicineOvarian cancerColorectal cancer

Abstract

fetched live from OpenAlex

INTRODUCTION AND IMPORTANCE: Endometrial cancer may disseminate through lymphatic channels to pelvic and retroperitoneal lymph nodes, through the bloodstream to the lungs, or through the peritoneal space to peritoneal surfaces. However, not all endometrial cancers involve all 3 sites for metastatic disease. CASE PRESENTATION: A patient with large volume of symptomatic recurrence of peritoneal metastases from endometrial cancer was subjected to additional surgery and both regional and systemic chemotherapy. All aspects of her disease and its treatment were studied. CLINICAL DISCUSSION: The primary malignancy was treated by a laparoscopic hysterectomy and bilateral salpingo-oophorectomy followed by intravaginal radiation. Large volume recurrent disease limited to the abdomen and pelvis was treated by complete cytoreductive surgery (CRS), hyperthermic intraperitoneal chemotherapy (HIPEC) and early postoperative intraperitoneal chemotherapy (EPIC). After recovery from surgery, systemic chemotherapy with cisplatin and paclitaxel was administered. The patient is now 25 months following treatment for recurrent cancer and free of disease. CONCLUSIONS: The possibility of complete resection of recurrent endometrial cancer combined with HIPEC, EPIC and systemic chemotherapy is a treatment option for selected patients.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.144
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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 teacher head, 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

Citations1
Published2021
Admission routes1
Has abstractyes

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