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Record W2163914042 · doi:10.6004/jnccn.2015.0029

Multimodality Approaches for the Curative Treatment of Esophageal Cancer

2015· review· en· W2163914042 on OpenAlexaffabout
Raymond Jang, Gail Darling, Rebecca Wong

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2015
Typereview
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineEsophageal cancerChemoradiotherapyEsophagusGastroesophageal JunctionModality (human–computer interaction)Treatment modalityGeneral surgerySurgeryCancerRadiation therapyRadiologyInternal medicineAdenocarcinoma

Abstract

fetched live from OpenAlex

Carcinoma of the esophagus and gastroesophageal junction tumors presenting with locoregional disease are potentially curable, although the cure rate is modest. Many different treatment approaches have been studied, with a multimodality approach associated with a 10% to 15% greater survival advantage compared with a single-modality approach. A systematic review was conducted to address 3 clinical questions: whether patients with resectable esophageal cancer should receive preoperative versus postoperative therapy, how to choose between these options, and whether surgery be avoided in patients who are candidates for both definitive chemoradiotherapy and definitive combined modality therapy. Recommendations from 3 recent treatment guidelines from Ontario, NCCN, and Belgium were consulted to address these questions.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.392
GPT teacher head0.483
Teacher spread0.091 · 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

Citations51
Published2015
Admission routes2
Has abstractyes

Explore more

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