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Record W2621024184 · doi:10.1200/jco.2016.71.6894

Do the American Society of Clinical Oncology Value Framework and the European Society of Medical Oncology Magnitude of Clinical Benefit Scale Measure the Same Construct of Clinical Benefit?

2017· article· en· W2621024184 on OpenAlexaffabout
Sierra Cheng, Erica McDonald, Matthew C. Cheung, Vanessa Sarah Arciero, Mahin Qureshi, Di Jiang, Doreen A. Ezeife, Mona Sabharwal, Alexandra Chambers, Dolly Han, Natasha B. Leighl, Kelley-Anne Sabarre, Kelvin Chan

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsCanadian Centre for Applied Research in Cancer Control
FundersEuropean Society for Medical OncologyAmerican Society of Clinical Oncology
KeywordsMedicineNiceIntraclass correlationClinical OncologyClinical trialInternal medicineFamily medicineOncologyCancer

Abstract

fetched live from OpenAlex

Purpose Whether the ASCO Value Framework and the European Society for Medical Oncology (ESMO) Magnitude of Clinical Benefit Scale (MCBS) measure similar constructs of clinical benefit is unclear. It is also unclear how they relate to quality-adjusted life-years (QALYs) and funding recommendations in the United Kingdom and Canada. Methods Randomized clinical trials of oncology drug approvals by the US Food and Drug Administration, European Medicines Agency, and Health Canada between 2006 and August 2015 were identified and scored using the ASCO version 1 (v1) framework, ASCO version 2 (v2) framework, and ESMO-MCBS by at least two independent reviewers. Spearman correlation coefficients were calculated to assess construct (between frameworks) and criterion validity (against QALYs from the National Institute for Health and Care Excellence [NICE] and the pan-Canadian Oncology Drug Review [pCODR]). Associations between scores and NICE/pCODR recommendations were examined. Inter-rater reliability was assessed using intraclass correlation coefficients. Results From 109 included randomized clinical trials, 108 ASCOv1, 111 ASCOv2, and 83 ESMO scores were determined. Correlation coefficients for ASCOv1 versus ESMO, ASCOv2 versus ESMO, and ASCOv1 versus ASCOv2 were 0.36 (95% CI, 0.15 to 0.54), 0.17 (95% CI, -0.06 to 0.37), and 0.50 (95% CI, 0.35 to 0.63), respectively. Compared with NICE QALYs, correlation coefficients were 0.45 (ASCOv1), 0.53 (ASCOv2), and 0.46 (ESMO); with pCODR QALYs, coefficients were 0.19 (ASCOv1), 0.20 (ASCOv2), and 0.36 (ESMO). None of the frameworks were significantly associated with NICE/pCODR recommendations. Inter-rater reliability was good for all frameworks. Conclusion The weak-to-moderate correlations of the ASCO frameworks with the ESMO-MCBS, as well as their correlations with QALYs and with NICE/pCODR funding recommendations, suggest different constructs of clinical benefit measured. Construct convergent validity with the ESMO-MCBS did not increase with the updated ASCO framework.

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)0.127
metaresearch head score (Gemma)0.353
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.353
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.009
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.002
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 head0.196
GPT teacher head0.462
Teacher spread0.266 · 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.

Study designNot applicable
DomainEvaluation
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

Citations62
Published2017
Admission routes2
Has abstractyes

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