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Record W4289756062 · doi:10.1177/26345161221115360

Treatment Options for Patients With Persistent Disease After Initial Endotherapy for Esophageal Neoplasia

2022· article· en· W4289756062 on OpenAlexaff
Clarence Wong, Kurian Joseph, Simon R. Turner

Bibliographic record

VenueForegut The Journal of the American Foregut Society · 2022
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDiseaseEsophagectomySurgeryEsophagusSalvage therapyDysplasiaEsophageal cancerIntensive care medicineInternal medicineCancerChemotherapy

Abstract

fetched live from OpenAlex

There are numerous treatment options for esophageal dysplasia and neoplasia. Initial endoscopic therapy is the standard of care for high-risk lesions of the esophagus. However, residual and recurrent disease may be seen after initial endoscopic treatment, and selection of the optimal salvage treatment may be critical in achieving cure. Patients may be high risk for esophagectomy after assessment of risk factors such as age, frailty, and nutritional status. A wide array of salvage endoscopic eradication therapies may also be available, but consideration should be given to the initial endoscopic treatment modality, patient related risk factors, and expertise at the regional center. Chemoradiation may also play a key role in treating high risk patients and should be planned in conjunction with surgery or endoscopic therapy. Multidisciplinary team care improves survival in high-risk patients with high-risk lesions. Consideration should be given to a multidisciplinary approach after the diagnosis of dysplastic and malignant esophageal disease.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueForegut The Journal of the American Foregut SocietySame topicEsophageal Cancer Research and TreatmentFrench-language works237,207