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Record W2048304021 · doi:10.1177/0269216311415454

Do beta-blockers alter dyspnea and fatigue in advanced lung cancer? A retrospective analysis

2011· article· en· W2048304021 on OpenAlexaffabout
Paul A. Cameron, Gregory R. Pond, John R. Goffin

Bibliographic record

VenuePalliative Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsMcMaster UniversityQueen's University
Fundersnot available
KeywordsMedicineCOPDLung cancerRetrospective cohort studyAnemiaInternal medicineHeart failureCoronary artery diseaseBeta blockerCardiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Dyspnea is common in lung cancer and may be partially attributable to increased ventilatory drive due to muscle weakness. The sympathetic component of this pathway might be mitigated by β-blockers. METHODS: A retrospective review of new patients with stage III-IV non-small lung cancer or any small cell lung cancer was undertaken to assess the impact of β-blocker use on dyspnea and fatigue. Data were abstracted for clinical characteristics, β-blocker use, and pre-treatment Edmonton Symptom Assessment System dyspnea and fatigue scores. RESULTS: Of 348 patients assessed, 202 met eligibility criteria. The median age was 67, 55.4% were female, 18.8% had chronic obstructive pulmonary disease (COPD), and 5.9% had active coronary artery disease. Over 60% of patients scored 4/10 or higher on their dyspnea and fatigue scores. While dyspnea and fatigue were moderately associated, no association was found between β-blocker use and either symptom. Recorded dosages of β-blockers were low. COPD was associated with dyspnea and fatigue, while anemia was associated with fatigue. CONCLUSIONS: Dyspnea and fatigue are prevalent and increased in the presence of COPD and anemia. No association between β-blocker use and dyspnea or fatigue scores was observed. This may be attributable to inadequate dosing or to retrospective bias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.339
Teacher spread0.301 · 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.

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

Citations7
Published2011
Admission routes2
Has abstractyes

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