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Record W1968027910 · doi:10.5539/cco.v4n1p1

Chemoradiation Compared to Surgery Alone in Patients With Non- Metastatic Esophageal Carcinoma

2014· article· en· W1968027910 on OpenAlexvenueno aff
Mohamed I. El-Sayed, Doaa W. Maximos

Bibliographic record

VenueCancer and Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEsophagectomyEsophageal cancerUnivariate analysisStage (stratigraphy)Medical recordInternal medicineCarcinomaLog-rank testSurgeryT-stageSurvival rateGastroenterologyCancerSurvival analysisMultivariate analysis

Abstract

fetched live from OpenAlex

Purpose: To assess overall survival (OS) of esophageal cancer patients treated either by esophagectomy or chemoradiation (CRT). Methods: The medical records of patients with non metastatic esophageal cancer, treated with esophagectomy and those treated with concurrent CRT were analyzed. For all patients, files were reviewed for age, sex, tumor site and type, grade, disease stage and survival. The Log- rank test was used to examine differences in OS rates. Results: The medical records of 90 patients were analyzed. After a median follow up of 20 months, 2-year OS rate for the whole group was 46%. There was significant differences in 2-year OS in favor of patients treated by concurrent CRT (55.4%) compared to those treated by surgery (31%) (p=0.016, HR:1.96, 95% CI: 1.13–3.38). Univariate analysis showed that patients in each treatment group, had comparable 2-year OS rates regarding patient’s age, gender, pathologic subtype, and histologic grade (p>0.05). Disease stage in each group and tumor site in CRT group significantly affected OS rates (p<0.05). Conclusions: Survival rates were statistically significant higher in patients treated with CRT than in those underwent esophagectomy. Prognostic factors that affected survival were disease stage in each treatment group and tumor site in CRT group.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.403
Teacher spread0.348 · 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 designObservational
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

Citations1
Published2014
Admission routes1
Has abstractyes

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