MétaCan
Menu
← Back to cohort

Province-wide analysis of patient reported outcomes for stage IV non-small cell lung cancer.

2021· article· en· W3167482687 on OpenAlexaffabout
Michael C. Tjong, Mark Doherty, Hendrick Tan, Wing C. Chan, Haoyu Zhao, Julie Hallet, Gail Darling, Biniam Kidane, Frances C. Wright, Alyson Mahar, Laura Davis, Victoria Delibasic, Ambika Parmar, Nicole Mittmann, Natalie G. Coburn, Alexander V. Louie

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsSunnybrook HospitalHealth Sciences CentreUniversity of TorontoPrincess Margaret Cancer CentreUniversity of ManitobaCentre for Addiction and Mental HealthSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineComorbidityLung cancerStage (stratigraphy)Depression (economics)NauseaAnxietyCancerPoisson regressionInternal medicinePolypharmacyPhysical therapyPopulationPsychiatry

Abstract

fetched live from OpenAlex

12092 Background: Stage IV NSCLC patients have significant disease and treatment-related morbidity. In Ontario, Canada, cancer patients complete Edmonton Symptom Assessment System (ESAS) questionnaires, a tool that elicits patients’ self-reported severity of common cancer-associated symptoms at clinical encounters. ESAS domains are: anxiety, depression, drowsiness, appetite, nausea, pain, shortness of breath, tiredness and well-being. The purpose of this study is to examine moderate-to-severe symptom burden in the 12 months following a diagnosis of stage IV NSCLC. Methods: Using administrative databases and unique encoded identifiers, stage IV NSCLC diagnosed between January 2007 and September 2018 were evaluated for symptom screening with ESAS in the 12 months following diagnosis. Proportion of patients reporting moderate-to-severe score (i.e. ESAS ≥4) in each domain within 12 months were calculated. Patients reporting moderate-to-severe within the different ESAS domains of were plotted over time. Multivariable (MV) Poisson regression models with potential covariates such as age, sex, Elixhauser comorbidity index, income quintiles, and lung cancer treatments received were constructed to identify factors associated with moderate-to-severe symptoms. Results: Of 22,799 stage IV NSCLC patients, 13,289 (58.3%) had completed ESAS (84,373 unique assessments) in the year following diagnosis. Patients with older age, high comorbidity, and not receiving active cancer therapy were less likely to complete ESAS. Most (94.4%) reported at least 1 moderate-to-severe score. Most prevalent moderate-to-severe ESAS symptoms within 12 months after diagnosis were tiredness (84.1%), lack of wellbeing (80.7%), low appetite (71.7%), and shortness of breath (67.8%); nausea was the least prevalent (34.6%). Most symptoms peaked at diagnosis and persisted in the year after diagnosis. On adjusted MV analyses, patients with high comorbidity, low income, and urban residency were associated with increased moderate-to-severe symptoms. Moderate-to-severe scores in all ESAS symptoms were associated with delivery of radiotherapy within 2 weeks prior, while moderate-to-severe nausea, drowsiness, tiredness, low appetite, and lack of wellbeing were associated with delivery of systemic therapy within preceding 2 weeks. Conclusions: In this population-based analysis of stage IV NSCLC PROs in the year following diagnosis, moderate-to-severe symptoms were highly prevalent and persistently high, underscoring the need to address supportive requirements in this at-risk population.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.689
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.006
Science and technology studies0.0010.000
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 head0.077
GPT teacher head0.489
Teacher spread0.412 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicLung Cancer Treatments and Mutations→French-language works237,207→