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Impact of rates of referral and systemic therapy on 1-year outcomes in metastatic NSCLC: A real-world population based study.

2019· article· en· W2947572398 on OpenAlexaffabout
Adam Fundytus, Yunting Fu, Lorraine Shack, Truong‐Minh Pham, Winson Y. Cheung

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer AgencyAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineInternal medicineReferralHazard ratioLung cancerCancerCancer registryStage (stratigraphy)Charlson comorbidity indexCohortPopulationProportional hazards modelOncologyComorbidityConfidence intervalFamily medicine

Abstract

fetched live from OpenAlex

e18238 Background: EGFR/ALK inhibitors and immunotherapy represent promising treatments for metastatic NSCLC, but patients must be referred to a cancer center (CC) to be considered for these treatments. Local referral rates to a CC for advanced pancreatic cancer are only 51%. We hypothesized that rates of referral for stage IV NSCLC are also low, thereby affecting survival. Methods: Using linked data from the provincial cancer registry, oncology specific EMR, administrative claims and vital statistics, we identified all patients diagnosed with stage IV NSCLC from 2009 to 2016 in Alberta, Canada. Demographics, Charlson Comorbidity Index (CCI), method of diagnosis, year of diagnosis and site of metastasis were compared between patients referred vs not referred (NR) to a CC and between those who received systemic therapy (ST) vs those who did not. A multivariable piecewise constant hazard model was constructed to estimate the hazard ratio (HR) of death in the 1st year. Results: We identified 9717 stage IV NSCLC patients among whom 65.8% and 34.2% were diagnosed pre and post 2012. In this cohort, 6907 (71%) were seen at a CC. Factors which predict referral to a cancer center are: dx by cytology (OR 6.81, 95% CI: 5.99-7.75; p < 0.001) or histology (OR 6.29, 95% CI: 5.40-7.34; p < 0.001) vs radiologic dx; Age < 70 (OR 1.87, 95% CI: 1.69-2.07; p < 0.001); dx after 2012 (OR 1.52, 95% Cl 1.36-1.70; p < 0.001) and CCI≤1 (OR 1.50, 95% Cl 1.34-1.68; p < 0.001). ST was administered to 2057(21.2%). Factors that predict ST are: dx by cytology (OR 6.48, 95% CI: 5.18-8.11; p < 0.001) or histology (OR 6.35.18 , 95% CI: 5.02-8.04; p < 0.001); Age < 70 (OR 2.55, 95% Cl 2.33-2.82; p < 0.001); CCI≤1 (OR 1.56, 95% Cl 1.39-1.76; p < 0.001); dx after 2012 (OR 1.28, 95% CI: 1.16-1.41 and female gender (OR 1.18 , 95% CI: 1.07-1.29; p < 0.001; p < 0.001). Within the 1st year post dx, HR for mortality was lower both in patients referred to a CC vs NR (HR 0.3, 95%CI .28-0.31, p < 0.0001) and patients receiving ST vs no ST (HR 0.3, 95% CI .28-0.32, p < 0.0001). Conclusions: Close to 1 in 3 patients with stage IV NSCLC were not referred to a CC even though referral and receipt of ST were associated with a significantly lower risk of death in the 1st year following diagnosis. Clear delineation and wide dissemination of appropriate referral pathways are needed to improve outcomes, especially among older patients.

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)0.004
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 score0.317
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.101
GPT teacher head0.483
Teacher spread0.382 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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