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Record W2994835730 · doi:10.2196/14359

Survival Rate of Gastric Cancer Patients in Jordan: Secondary Data Analysis

2019· article· en· W2994835730 on OpenAlexvenueno aff
Ashraf Aqel, Yousef Khader, Kamal Arqoub, Omar Nimri

Bibliographic record

VenueJMIR Public Health and Surveillance · 2019
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerCancer registrySurvival rateInternal medicineSurvival analysisStage (stratigraphy)Mortality rateDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Gastric cancer accounts for 2.7% of all newly diagnosed cancer cases in Jordan. OBJECTIVE: The aim of this study was to calculate the survival rate and its determinants among Jordanian patients who were diagnosed with gastric cancer between 2010 and 2014. METHODS: A descriptive study was conducted based on secondary analysis of data from the Jordan Cancer Registry during the period of 2010-2014. Only cancer-related deaths were recorded as "death" in the survival analysis. RESULTS: A total of 1388 new cases of gastric cancer were recorded between 2010 and 2014. Of these, 872 (62.8%) were Jordanians and 60.5% were males. The mean age at diagnosis was 58.9 years and the median follow-up time was 1.6 years. The 5-year survival rate decreased significantly from 89% in patients with well-differentiated cancer to 32% in patients with poorly differentiated cancer (P=.005). The overall 5-year survival rate was 37.7% and the median survival was 1.48 years (95% CI 1.179-1.783). The 5-year survival rate decreased significantly with increasing age and with advanced stage of the disease: the 5-year survival rate was 75% for localized-stage, 48% for regional-stage, and 22.7% for distant-metastasis disease (P=.005). CONCLUSIONS: This study showed that the overall 5-year survival rate among patients with gastric cancer in Jordan between 2010 and 2014 was 37.7%, which is higher than the reported rates from different countries in the Eastern Mediterranean region such as Egypt.

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)0.001
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 score0.030
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.035
GPT teacher head0.324
Teacher spread0.289 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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