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Record W3216883981 · doi:10.3390/curroncol28060412

Impact of Value Frameworks on the Magnitude of Clinical Benefit: Evaluating a Decade of Randomized Trials for Systemic Therapy in Solid Malignancies

2021· article· en· W3216883981 on OpenAlexaffvenue
Ellen Cusano, Chelsea Wong, Eddy Taguedong, Marcus Vaska, Tasnima Abedin, Nancy Nixon, Safiya Karim, Patricia A. Tang, Daniel Yick Chin Heng, Doreen A. Ezeife

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsMcGill University Health CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineRandomized controlled trialClinical endpointQuality of life (healthcare)Internal medicineClinical trialOncologySystemic therapySurrogate endpointOverall survivalCancerProgression-free survivalIntensive care medicineBreast cancer

Abstract

fetched live from OpenAlex

In the era of rapid development of new, expensive cancer therapies, value frameworks have been developed to quantify clinical benefit (CB). We assessed the evolution of CB since the 2015 introduction of The American Society of Clinical Oncology and The European Society of Medical Oncology value frameworks. Randomized clinical trials (RCTs) assessing systemic therapies for solid malignancies from 2010 to 2020 were evaluated and CB (Δ) in 2010–2014 (pre-value frameworks (PRE)) were compared to 2015–2020 (POST) for overall survival (OS), progression-free survival (PFS), response rate (RR), and quality of life (QoL). In the 485 studies analyzed (12% PRE and 88% POST), the most common primary endpoint was PFS (49%), followed by OS (20%), RR (12%), and QoL (6%), with a significant increase in OS and decrease in RR as primary endpoints in the POST era (p = 0.011). Multivariable analyses revealed significant improvement in ΔOS POST (OR 2.86, 95% CI 0.46 to 5.26, p = 0.02) while controlling for other variables. After the development of value frameworks, median ΔOS improved minimally. The impact of value frameworks has yet to be fully realized in RCTs. Efforts to include endpoints shown to impact value, such as QoL, into clinical trials are warranted.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.364
metaresearch head score (Gemma)0.477
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3640.477
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0020.004
Science and technology studies0.0010.004
Scholarly communication0.0070.007
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.425
GPT teacher head0.520
Teacher spread0.095 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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

Citations0
Published2021
Admission routes2
Has abstractyes

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