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Record W4378214136 · doi:10.1136/bmj-2022-072108

Recommendations for the development, implementation, and reporting of control interventions in efficacy and mechanistic trials of physical, psychological, and self-management therapies: the CoPPS Statement

2023· article· en· W4378214136 on OpenAlexafffund
David Hohenschurz‐Schmidt, Lene Vase, Whitney Scott, Marco Annoni, Oluwafemi K Ajayi, Jürgen Barth, Kim L. Bennell, Chantal Berna, Joel E. Bialosky, F BRAITHWAITE, Nanna Brix Finnerup, Amanda C de C Williams, Elisa Carlino, Francesco Cerritelli, Aleksander Chaibi, Dan Cherkin, Luana Colloca, Pierre Côté, Beth D. Darnall, Roni Evans, Laurent Fabre, Vanda Faria, Simon French, Heike Gerger, Winfried Häuser, Rana S. Hinman, Dien Ho, Thomas Janssens, Karin Jensen, Chris Johnston, Sigrid Juhl Lunde, Francis J. Keefe, Robert D. Kerns, Helen Koechlin, Alice Kongsted, Lori A. Michener, Daniel E. Moerman, Frauke Musial, David Newell, Michael K. Nicholas, Tonya M. Palermo, Sara Palermo, Kaya J. Peerdeman, Esther Pogatzki‐Zahn, Aaron A. Puhl, Lisa Roberts, Giacomo Rossettini, Susan Tomczak Matthiesen, Martin Underwood, Paul Vaucher, Jan Vollert, Karolina Wartolowska, Katja Weimer, Christoph Werner, Andrew S.C. Rice, Jerry Draper‐Rodi

Bibliographic record

VenueBMJ · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsHand and Upper Limb ClinicOntario Tech University
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthBerner FachhochschuleAdvanced Scientific Computing ResearchNSW Ministry of HealthVetenskapsrådetBundesministerium für Bildung und ForschungFlinders UniversityUniversity of SydneySydney Medical SchoolSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche ForschungsgemeinschaftNovartis PharmaEuropean CommissionVersus ArthritisStowers Institute for Medical ResearchAustralian GovernmentGemeinsame BundesausschussUniversity of OxfordEuropean Chiropractors' UnionUniversiteit LeidenNational Health and Medical Research CouncilRoyal British LegionEli Lilly and CompanyEuropean Federation of Pharmaceutical Industries and AssociationsInternational Association for the Study of PainBiogenSocial Sciences and Humanities Research Council of CanadaUniversität BaselPatient-Centered Outcomes Research InstituteNational Institute for Health and Care ResearchMedical Research CouncilTeva Pharmaceutical IndustriesAustralian Pain SocietyAmerican Cancer SocietyNational Institute on Drug AbuseVertex PharmaceuticalsDirectorate for Biological SciencesImperial College LondonNational Science Foundation
KeywordsPsychological interventionRigourChecklistContext (archaeology)MedicineTransparency (behavior)Intervention (counseling)Alternative medicineControl (management)Quality (philosophy)Management sciencePsychologyApplied psychologyComputer scienceNursingPathology

Abstract

fetched live from OpenAlex

Control interventions (often called “sham,” “placebo,” or “attention controls”) are essential for studying the efficacy or mechanism of physical, psychological, and self-management interventions in clinical trials. This article presents core recommendations for designing, conducting, and reporting control interventions to establish a quality standard in non-pharmacological intervention research. A framework of additional considerations supports researchers’ decision making in this context. We also provide a reporting checklist for control interventions to enhance research transparency, usefulness, and rigour.

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

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)0.773
metaresearch head score (Gemma)0.897
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7730.897
Meta-epidemiology (narrow)0.0050.008
Meta-epidemiology (broad)0.0090.021
Bibliometrics0.0150.013
Science and technology studies0.0060.017
Scholarly communication0.0150.021
Open science0.0150.011
Research integrity0.0400.037
Insufficient payload (model declined to judge)0.0080.010

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 head0.284
GPT teacher head0.497
Teacher spread0.213 · 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 designTheoretical or conceptual
DomainReporting
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

Citations103
Published2023
Admission routes2
Has abstractyes

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Same venueBMJSame topicPain Management and Placebo EffectFrench-language works237,207