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Record W4390508296 · doi:10.1093/jscr/rjad590

Palliative management of a malignant tracheoesophageal fistula using repeat endobronchial laser debridement and esophageal stenting

2023· article· en· W4390508296 on OpenAlexaff
Geraint Berger, Daniel French

Bibliographic record

VenueJournal of Surgical Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsVictoria General HospitalDalhousie University
Fundersnot available
KeywordsMedicineTracheoesophageal fistulaEsophagogastroduodenoscopyEsophageal stentStentSurgeryDysphagiaBronchoscopyEsophageal strictureRadiologyFistulaEsophagusGastrostomyEndoscopy

Abstract

fetched live from OpenAlex

A 71-year-old female presented with progressive dysphagia and unexplained weight loss. Computed tomography and esophagogastroduodenoscopy (EGD) revealed invasive esophageal squamous cell carcinoma, which was initially treated with local radiation and esophageal stenting. Over the next year, the patient experienced multiple symptoms and hospital admissions consistent with a malignant tracheoesophageal fistula, despite negative findings on imaging, bronchoscopy, and EGD. Prophylactic antibiotics were initiated based on symptomatology to prevent septic episodes. Stent erosion into the membranous trachea was eventually observed. Neodymium-yttrium-aluminum-garnet laser bronchoscopy was used periodically to debulk the invading tumor around the stent. A percutaneous endoscopic gastrostomy tube was also inserted to facilitate enteral nutrition and avoid aspiration pneumonia. The patient reported significant improvements in respiratory symptoms following each laser debridement and has progressed well beyond the life expectancy associated with malignant tracheoesophageal fistula.

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)0.002
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 score0.004
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
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 head0.034
GPT teacher head0.308
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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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