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Record W2922184861 · doi:10.1093/jcag/gwz006.258

A259 TREATMENTS AND OUTCOMES IN PATIENTS WITH LUNG CANCER AND CO-MORBID CIRRHOSIS IN ONTARIO FROM 2007–2016: A POPULATION-BASED STUDY

2019· article· en· W2922184861 on OpenAlexaffabout
Sanjay Mishra, Yvonne DeWit, Maya Djerboua, Jennifer A. Flemming

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineLung cancerInternal medicineCirrhosisPopulationCancerCancer registryProportional hazards modelSurgeryOncology

Abstract

fetched live from OpenAlex

Lung cancer is the most common cause of cancer-related mortality among Canadians. Patients with lung cancer and cirrhosis have historically been excluded from clinical trials evaluating cancer treatment efficacy due to perceived perioperative risk and hepatotoxicity. Thus, outcomes in this patient population are unknown. Given the increasing burden of cirrhosis in Canada, this study aimed to describe lung cancer treatments and outcomes in patients with primary lung cancer and co-morbid cirrhosis. Aim 1: To describe the types of treatment received (surgery, chemotherapy, and radiotherapy) in patients with lung cancer and cirrhosis stratified by cirrhosis status (compensated vs. decompensated). Aim 2: To describe cancer-specific survival (CSS) in patients with lung cancer and cirrhosis stratified by cirrhosis status (compensated vs. decompensated). We conducted a retrospective population-based cohort study using the linked administrative databases holdings of ICES. Adult patients with a diagnosis of primary lung cancer from 2007–2016 were identified based on administrative coding from the Ontario Cancer Registry. Those with co-morbid cirrhosis were identified and sorted as compensated or decompensated by validated case definitions. Baseline demographic characteristics and the receipt of surgery, chemotherapy and radiotherapy were described. Cancer-specific survival (CSS) was evaluated using Kaplan Meier curves and compared by log-rank test. The association between cirrhosis and CCS after adjusting for confounders was evaluated using multivariate Cox proportional hazards regression. 42,857 patients with primary lung cancer were identified (48% female, mean age of 69 years, 47% stage IV cancer, 5-year overall survival 19%). 1,244 patients had cirrhosis with 156 being decompensated. Patients with cirrhosis were more likely young, male, of lower socioeconomic status, and higher co-morbidity than those without. Patients with cirrhosis had similar surgical rates to those without (56% vs. 56%, P=0.31) but less often had chemotherapy (34% vs. 39%, P <.001) or radiotherapy (55% vs. 58%, P<.001). Overall, the median 5-year CCS was 341 days (IQR 333–349) and was 40 days shorter in those with cirrhosis (median 311 days [IQR 263–357], P <.001). After multivariate Cox regression, patients with compensated cirrhosis had a similar risk of cancer-related mortality to those without cirrhosis (HR 1.08, 95% CI 0.99–1.16, P=0.06) but those with decompensated disease had higher mortality risk (HR 1.36, 95% CI 1.12–1.65, P<.001). In this study, patients with lung cancer and cirrhosis have worse CCS than those without cirrhosis but this appears to be isolated to those with decompensated disease. These results can be used in treatment decisions in patients with lung cancer and co-morbid cirrhosis. Thomas M. and Louise A. Brown Research Studentship (SM) and SEAMO New Clinician Scientist Development Program (JAF)

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.046
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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 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

Citations0
Published2019
Admission routes2
Has abstractyes

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