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Record W2534459715 · doi:10.1016/s1470-2045(16)30510-1

Analysing data from patient-reported outcome and quality of life endpoints for cancer clinical trials: a start in setting international standards

2016· review· en· W2534459715 on OpenAlexaff
Andrew Bottomley, Madeline Pe, Jeff A. Sloan, Ethan Basch, Franck Bonnetain, Melanie Calvert, Alicyn Campbell, Charles S. Cleeland, Kim Cocks, Laurence Collette, Amylou C. Dueck, Nancy Devlin, Hans‐Henning Flechtner, Carolyn Gotay, Eva Greimel, Ingolf Griebsch, Mogens Grøenvold, Jean‐François Hamel, Madeleine King, Paul G. Kluetz, Michael Koller, Daniel C. Malone, Francesca Martinelli, Sandra A. Mitchell, Carol M. Moinpour, Jammbe Musoro, Daniel O’Connor, Kathy Oliver, Elisabeth Piault‐Louis, Martine Piccart, Francisco Pimentel, Chantal Quinten, Jaap C. Reijneveld, Christoph Schürmann, Ashley Wilder Smith, Katherine M Soltys, Martin Taphoorn, Galina Velikova, Corneel Coens

Bibliographic record

VenueThe Lancet Oncology · 2016
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsHealth CanadaUniversity of British Columbia
FundersBoehringer Ingelheim
KeywordsClinical trialMedicineQuality of life (healthcare)CancerQuality (philosophy)Health careFamily medicineNursing

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)0.294
metaresearch head score (Gemma)0.478
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.706
Threshold uncertainty score0.870

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2940.478
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.011
Bibliometrics0.0070.010
Science and technology studies0.0010.005
Scholarly communication0.0090.009
Open science0.0060.005
Research integrity0.0060.015
Insufficient payload (model declined to judge)0.0030.001

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 head0.727
GPT teacher head0.577
Teacher spread0.150 · 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
GenreReview

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

Citations198
Published2016
Admission routes1
Has abstractno

Explore more

Same venueThe Lancet OncologySame topicEconomic and Financial Impacts of CancerFrench-language works237,207