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Record W4392294166 · doi:10.1038/s41591-024-02827-9

Recommendations to address respondent burden associated with patient-reported outcome assessment

2024· article· en· W4392294166 on OpenAlexafffund
Olalekan Lee Aiyegbusi, Samantha Cruz Rivera, Jessica Roydhouse, Paul Kamudoni, Yvonne Alder, Nicola Anderson, R. Mitchell Baldwin, Vishal Bhatnagar, Jennifer Black, Andrew Bottomley, Michael Brundage, David Cella, Philip Collis, Elin-Haf Davies, Alastair K. Denniston, Fabio Efficace, Adrian Gardner, Ari Gnanasakthy, Robert Golub, Sarah Hughes, Flic Jeyes, Scottie Kern, Bellinda L. King‐Kallimanis, Antony P. Martin, Christel McMullan, Rebecca Mercieca‐Bebber, João Monteiro, John Devin Peipert, Juan Carlos Quijano-Campos, Chantal Quinten, Khadija Rantell, Antoine Regnault, Maxime Sasseville, Liv Marit Valen Schougaard, Roya Sherafat‐Kazemzadeh, Claire Snyder, Angela M. Stover, Rav Verdi, Roger Wilson, Melanie Calvert

Bibliographic record

VenueNature Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsQueen's UniversityHealth Canada
FundersDaiichi Sankyo EuropeUCB PharmaNational Institute for Health Research Applied Research Collaboration WestMacmillan Cancer SupportUniversity of Texas MD Anderson Cancer CenterAstraZenecaEuropean CommissionGenentechFaculty of Medicine and Health, University of SydneyU.S. Food and Drug AdministrationBarts Health NHS TrustHealth CanadaAstellas PharmaQueen's UniversityUniversity of SydneyAston UniversityEuropean Regional Development FundMerck KGaAECOG-ACRIN Cancer Research GroupJohns Hopkins UniversityFeinberg School of MedicineLUNGevity FoundationAnthony NolanNational Institutes of HealthMenzies Institute for Medical ResearchUniversity Hospitals Birmingham NHS Foundation TrustSarcoma UKNational Institute for Health and Care ResearchQueen Mary University of LondonBirmingham Biomedical Research CentrePfizerUniversity of TasmaniaNorthwestern UniversityPatient-Centered Outcomes Research InstituteIncyteVeloxis PharmaceuticalsBrown UniversityEuroQol Research FoundationAmerican College of Radiology Imaging NetworkNational Cancer InstituteGilead SciencesSurgical Reconstruction and Microbiology Research CentreUK Research and InnovationBristol-Myers SquibbEli Lilly and CompanyNational Health and Medical Research CouncilAmgenSeagen
KeywordsRespondentMedicineOutcome (game theory)Patient-reported outcomePolitical scienceNursingQuality of life (healthcare)

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)0.393
metaresearch head score (Gemma)0.695
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.748

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3930.695
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0050.005
Science and technology studies0.0060.006
Scholarly communication0.0120.012
Open science0.0080.008
Research integrity0.0160.017
Insufficient payload (model declined to judge)0.0140.005

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 head0.123
GPT teacher head0.513
Teacher spread0.390 · 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 designQualitative
DomainMethods
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

Citations116
Published2024
Admission routes2
Has abstractno

Explore more

Same venueNature MedicineSame topicDelphi Technique in ResearchFrench-language works237,207