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Record W3003402209 · doi:10.1016/s1470-2045(19)30790-9

International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials: recommendations of the SISAQOL Consortium

2020· review· en· W3003402209 on OpenAlexafffund
Corneel Coens, Madeline Pe, Amylou C. Dueck, Jeff A. Sloan, Ethan Basch, Melanie Calvert, Alicyn Campbell, Charles S. Cleeland, Kim Cocks, Laurence Collette, Nancy Devlin, Lien Dorme, Hans‐Henning Flechtner, Carolyn Gotay, Ingolf Griebsch, Mogens Grøenvold, Madeleine King, Paul G. Kluetz, Michael Koller, Daniel C. Malone, Francesca Martinelli, Sandra A. Mitchell, Jammbe Musoro, Daniel O’Connor, Kathy Oliver, Elisabeth Piault‐Louis, Martine Piccart, Chantal Quinten, Jaap C. Reijneveld, Christoph Schürmann, Ashley Wilder Smith, Katherine M Soltys, Martin Taphoorn, Galina Velikova, Andrew Bottomley

Bibliographic record

VenueThe Lancet Oncology · 2020
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsHealth CanadaUniversity of British Columbia
FundersHealth CanadaGenentechNational Cancer InstituteNational Institutes of HealthEuropean Organisation for Research and Treatment of CancerU.S. Food and Drug AdministrationNational Institute for Health and Care ResearchBoehringer Ingelheim
KeywordsTerminologyMedicineQuality of life (healthcare)Missing dataAlternative medicineRandomized controlled trialQuality (philosophy)MEDLINEClinical trialManagement scienceMedical physicsFamily medicineMedical educationComputer scienceNursingPolitical sciencePathologyEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.477
metaresearch head score (Gemma)0.569
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.523
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4770.569
Meta-epidemiology (narrow)0.0060.006
Meta-epidemiology (broad)0.0280.048
Bibliometrics0.0190.024
Science and technology studies0.0030.010
Scholarly communication0.0190.005
Open science0.0190.008
Research integrity0.0220.037
Insufficient payload (model declined to judge)0.0130.010

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 head0.736
GPT teacher head0.602
Teacher spread0.134 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations306
Published2020
Admission routes2
Has abstractno

Explore more

Same venueThe Lancet OncologySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207