MétaCan
Menu
Back to cohort
Record W2900603576 · doi:10.14740/wjon1163

Serum Carcinoembryonic Antigen Level Predicts Cancer-Specific Outcomes of Resected Non-Small Cell Lung Cancer With Interstitial Pneumonia

2018· article· en· W2900603576 on OpenAlexvenueno aff
Masaki Tomita, Takanori Ayabe, Ryo Maeda, Kunihide Nakamura

Bibliographic record

VenueWorld Journal of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCarcinoembryonic antigenInternal medicineLung cancerOncologyStage (stratigraphy)Univariate analysisProportional hazards modelCancerGastroenterologyMultivariate analysis

Abstract

fetched live from OpenAlex

BACKGROUND: It has been well accepted that the prognosis of non-small cell lung cancer (NSCLC) patients with interstitial pneumonia (IP) is significantly poor. However, there are only a few studies that indicated the prognostic factors, especially tumor markers, among NSCLC patients with IP. METHODS: Forty-one NSCLC patients with IP who underwent surgery at our institution were included. Patients died of other diseases including postoperative acute exacerbation (AE) of IP were excluded. Univariate and multivariate analyses were calculated by the Cox proportional hazards regression model. RESULTS: The 5-year cancer-specific survival of overall and stage I patients were 37.4% and 39.2%, respectively. The 5-year cancer-specific survival of patients with high serum carcinoembryonic antigen (CEA) level was 9.4%, while that with normal serum CEA level was 55.6%. However, serum cytokeratin-19 fragment (CYFRA 21-1) and squamous cell carcinoma-related antigen (SCC) levels were not associated with patients' survival. Furthermore, serum CEA level was significantly associated with poorer cancer-specific survival in univariate and multivariate analyses. CONCLUSIONS: This study demonstrated that serum CEA level might serve as an efficient prognostic indicator after surgery in NSCLC with IP.

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 score0.021
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.373
Teacher spread0.324 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of OncologySame topicLung Cancer Research StudiesFrench-language works237,207