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Record W4311917936 · doi:10.1016/j.annonc.2022.12.004

Methodological and reporting standards for quality-of-life data eligible for European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) credit

2022· article· en· W4311917936 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Oncology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsQueen's University
FundersGenentechIpsenEuropean Society for Medical OncologyChugai PharmaceuticalSeagenRegeneron PharmaceuticalsServierG1 TherapeuticsAmgenPfizerWorld Health OrganizationRadius HealthAstraZenecaEli Lilly and Company
KeywordsMedicineChecklistQuality of life (healthcare)Clinical trialClinical endpointScale (ratio)Test (biology)Medical physicsIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) has been developed to grade clinical benefit of cancer therapies. Improvement in quality of life (QoL) is considered relevant, especially in the non-curative setting. This is reflected by an upgrade of the preliminary ESMO-MCBS score if QoL is improved compared to the control arm or a downgrade if an improvement in progression-free survival is not paralleled by an improvement in QoL or overall survival. Given the importance of QoL for the final score, a need to ensure the robustness of QoL data was recognised. DESIGN: A checklist was created based on existing guidelines for QoL research. Field testing was carried out using clinical trials that either received an adjustment of the preliminary ESMO-MCBS score based on QoL or had QoL as the primary endpoint. Several rounds of revision and re-testing of the checklist were undertaken until a final consensus was reached. RESULTS: The final checklist consists of four items and can be applied if three prerequisites are met: (i) QoL is at least a secondary endpoint, (ii) evidence of reliability and validity of the instrument is provided, and (iii) a statistically and clinically significant improvement in QoL is observed. The four items on the checklist pertain to the (i) hypothesis, (ii) compliance and missing data, (iii) presentation of the results, and (iv) statistical and clinical relevance. Field testing revealed that a clear QoL hypothesis and correction for multiple testing were mostly lacking, while the main statistical method was always described. CONCLUSIONS: Implementation of the ESMO-MCBS QoL checklist will facilitate objective and transparent decision making on QoL data within the ESMO-MCBS scoring process. Trials published until 1 January 2025 will have to meet the prerequisites and at least two items for crediting QoL benefit in the final ESMO-MCBS score. Trials published thereafter will have to meet all four items.

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.

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)0.057
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.731
GPT teacher head0.575
Teacher spread0.157 · 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