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Interpretation of chronic pain clinical trial outcomes: IMMPACT recommended considerations

2020· article· en· W3033940508 on OpenAlexaff
Shannon M. Smith, Robert H. Dworkin, Dennis C. Turk, Michael P. McDermott, Christopher Eccleston, John T. Farrar, Michael C. Rowbotham, Zubin Bhagwagar, Laurie B. Burke, Penney Cowan, Susan S. Ellenberg, Scott Evans, Roy Freeman, Louis P. Garrison, Smriti Iyengar, Alejandro R. Jadad, Mark P. Jensen, Roderick Junor, Cornelia Kamp, Nathaniel P. Katz, J. Patrick Kesslak, Ernest A. Kopecky, Dmitri Lissin, John D. Markman, Philip J. Mease, Alec O'connor, Kushang V. Patel, Srinivasa N. Raja, Cristina Sampaio, David Schoenfeld, Jasvinder A. Singh, Ilona Steigerwald, Vibeke Strand, Leslie Tive, Jeffrey Tobias, Ajay D. Wasan, Hilary Wilson

Bibliographic record

VenuePain · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersDaiichi Sankyo EuropeNational Institute of Allergy and Infectious DiseasesU.S. Food and Drug AdministrationNational Institutes of HealthAstellas PharmaVoyager TherapeuticsACADIA PharmaceuticalsCelgenePTC TherapeuticsNew York State Stem Cell ScienceUniversity of WashingtonEli Lilly and CompanyAllerganAstraZenecaMultiple Sclerosis SocietyNovartis Pharmaceuticals CorporationAdministration for Community LivingDepomedAlnylam PharmaceuticalsAlexion PharmaceuticalsNational Institute on AgingaTyrAmgenBiogenPfizerWashington State UniversityGlaxoSmithKline
KeywordsRandomized controlled trialMedicineAnalgesicClinical trialChronic painAddictionPhysical therapyIntensive care medicineMedical physicsPsychiatrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Interpreting randomized clinical trials (RCTs) is crucial to making decisions regarding the use of analgesic treatments in clinical practice. In this article, we report on an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) consensus meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks, the purpose of which was to recommend approaches that facilitate interpretation of analgesic RCTs. We review issues to consider when drawing conclusions from RCTs, as well as common methods for reporting RCT results and the limitations of each method. These issues include the type of trial, study design, statistical analysis methods, magnitude of the estimated beneficial and harmful effects and associated precision, availability of alternative treatments and their benefit-risk profile, clinical importance of the change from baseline both within and between groups, presentation of the outcome data, and the limitations of the approaches used.

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 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)0.089
metaresearch head score (Gemma)0.101
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0890.101
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.583
GPT teacher head0.510
Teacher spread0.072 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
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

Citations181
Published2020
Admission routes1
Has abstractyes

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