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Record W4313456906 · doi:10.2147/ijwh.s366680

Inoperable Bowel Obstruction in Ovarian Cancer: Prevalence, Impact and Management Challenges

2022· review· en· W4313456906 on OpenAlexaff
Eduardo González-Ochoa, Husam Alqaisi, Gita Bhat, Nazlin Jivraj, Stéphanie Lheureux

Bibliographic record

VenueInternational Journal of Women s Health · 2022
Typereview
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBowel obstructionIntensive care medicineLife expectancyQuality of life (healthcare)Colorectal cancerParenteral nutritionMultidisciplinary approachGastrostomyPsychological interventionOvarian cancerPalliative careCancerSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Malignant bowel obstruction (MBO) is one of the most severe complications in patients with advanced ovarian cancer, with an estimated incidence up to 50%. Its presence is related to poor prognosis and a life expectancy measured in weeks for inoperable cases. Symptoms are usually difficult to manage and often require hospitalization, which carries a high burden on patients, caregivers and the healthcare system. Management is complex and requires a multidisciplinary approach to improve clinical outcomes. Patients with inoperable MBO are treated medically with analgesics, antiemetics, steroids and antisecretory agents. Parenteral nutrition and gut decompression with nasogastric tube, venting gastrostomy or stenting may be used as supportive therapy. Treatment decision-making is challenging and often based on clinical expertise and local policies, with lack of high-quality evidence to optimally standardize management. The present review summarizes current literature on inoperable bowel obstruction in ovarian cancer, focusing on epidemiology, prognostic factors, clinical outcomes, medical management, multidisciplinary interventions and quality of life.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.421
Teacher spread0.341 · 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 designOther design
Domainnot available
GenreReview

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

Citations15
Published2022
Admission routes1
Has abstractyes

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