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Record W4381795442 · doi:10.3390/curroncol30070447

Endoscopic Stenting for Malignant Dysphagia in Patients with Esophageal Cancer

2023· review· en· W4381795442 on OpenAlexvenueno aff
Ryu Ishihara

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDysphagiaRadiation therapyPerforationStentGastrostomyEsophageal strictureEsophageal cancerAdverse effectEsophagusSurgeryPercutaneous endoscopic gastrostomyRadiologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Malignant dysphagia is a common problem in patients with esophageal cancer. Endoscopic stenting can resolve dysphagia caused by malignant stricture; however, controversy exists regarding the use of esophageal stenting for the treatment of malignant stricture, including whether stenting or radiotherapy is superior, whether stenting before or after radiotherapy is safe, whether stenting before or after chemotherapy is safe, and whether low-radial-force stents are safer than conventional stents. Among treatment options for malignant dysphagia, stenting may have some disadvantages in terms of pain relief and the risk of adverse events compared with radiotherapy and in terms of survival compared with gastrostomy. Additionally, the risk of stent-related adverse events is significantly associated with prior radiotherapy. The risk of perforation is especially high when a radiation dose of >40 Gy is delivered to the esophagus after stenting, whereas perforation is not associated with prior chemotherapy or additional chemotherapy after stenting. Nevertheless, stenting remains an important palliative option, especially for patients with a short life expectancy and a strong desire for oral intake, because stenting can facilitate a more rapid improvement in dysphagia than radiotherapy or gastrostomy. The application of a low-radial-force stent should be considered to reduce the risk of adverse events, especially in patients with prior radiotherapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.191
GPT teacher head0.465
Teacher spread0.274 · 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.

Study designNot applicable
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

Citations11
Published2023
Admission routes1
Has abstractyes

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