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Record W4281479985 · doi:10.1136/bmjopen-2021-053417

Reporting quality of clinical trial protocols: a repeated cross-sectional study about the Adherence to SPIrit Recommendations in Switzerland, CAnada and GErmany (ASPIRE-SCAGE)

2022· article· en· W4281479985 on OpenAlex
Dmitry Gryaznov, Belinda von Niederhäusern, Benjamin Speich, Benjamin Kasenda, Elena Ojeda–Ruiz, Anette Blümle, Stefan Schandelmaier, Dominik Mertz, Ayodele Odutayo, Yuki Tomonaga, Alain Amstutz, Christiane Pauli‐Magnus, Viktoria Gloy, Szimonetta Lohner, Karin Bischoff, Katharina Wollmann, Laura Rehner, Joerg J Meerpohl, Alain Nordmann, Katharina Klatte, Nilabh Ghosh, Ala Taji Heravi, Jacqueline Wong, Ngai Chow, Patrick Jiho Hong, Kimberly A McCord - De Iaco, Sirintip Sricharoenchai, Jason W. Busse, Arnav Agarwal, Ramon Saccilotto, Matthias Schwenkglenks, Giusi Moffa, Lars G. Hemkens, Sally Hopewell, Erik von Elm, Matthias Briel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBMJ Open · 2022
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of OttawaCentre for Disability Prevention and RehabilitationCanadian Memorial Chiropractic CollegeMcMaster UniversityImpact
FundersUniversity of OxfordBundesamt für GesundheitAlexander von Humboldt-StiftungAlbert-Ludwigs-Universität FreiburgSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicineChecklistProtocol (science)Randomized controlled trialClinical trialFamily medicineConsolidated Standards of Reporting TrialsCross-sectional studyGuidelineTrial registrationAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Comprehensive protocols are key for the planning and conduct of randomised clinical trials (RCTs). Evidence of low reporting quality of RCT protocols led to the publication of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist in 2013. We aimed to examine the quality of reporting of RCT protocols from three countries before and after the publication of the SPIRIT checklist. DESIGN: Repeated cross sectional study. SETTING: Swiss, German and Canadian research ethics committees (RECs). PARTICIPANTS: RCT protocols approved by RECs in 2012 (n=257) and 2016 (n=292). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were the proportion of reported SPIRIT items per protocol and the proportion of trial protocols reporting individual SPIRIT items. We compared these outcomes in protocols approved in 2012 and 2016, and built regression models to explore factors associated with adherence to SPIRIT. For each protocol, we also extracted information on general trial characteristics and assessed whether individual SPIRIT items were reported RESULTS: The median proportion of reported SPIRIT items among RCT protocols showed a non-significant increase from 72% (IQR, 63%-79%) in 2012 to 77% (IQR, 68%-82%) in 2016. However, in a preplanned subgroup analysis, we detected a significant improvement in investigator-sponsored protocols: the median proportion increased from 64% (IQR, 55%-72%) in 2012 to 76% (IQR, 64%-83%) in 2016, while for industry-sponsored protocols median adherence was 77% (IQR 72%-80%) for both years. The following trial characteristics were independently associated with lower adherence to SPIRIT: single-centre trial, no support from a clinical trials unit or contract research organisation, and investigator-sponsorship. CONCLUSIONS: In 2012, industry-sponsored RCT protocols were reported more comprehensively than investigator-sponsored protocols. After publication of the SPIRIT checklist, investigator-sponsored protocols improved to the level of industry-sponsored protocols, which did not improve.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.119
metaresearch head score (Gemma)0.147
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1190.147
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 science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.863
GPT teacher head0.743
Teacher spread0.119 · 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