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Record W2223453682 · doi:10.1097/ccm.0000000000001369

Premature Discontinuation of Randomized Trials in Critical and Emergency Care

2015· article· en· W2223453682 on OpenAlexafffundabout
Stefan Schandelmaier, Erik von Elm, John J. You, Anette Blümle, Yuki Tomonaga, François Lamontagne, Ramon Saccilotto, Alain Amstutz, Theresa Bengough, Joerg J Meerpohl, Mihaela Stegert, Kelechi Kalu Olu, Kari A.O. Tikkinen, Ignacio Neumann, Alonso Carrasco‐Labra, Markus Faulhaber, Sohail Mulla, Dominik Mertz, Elie A. Akl, Xin Sun, Dirk Bassler, Jason W. Busse, Ignacio Ferreira‐González, Alain Nordmann, Viktoria Gloy, Heike Raatz, Lorenzo Moja, Rachel Rosenthal, Shanil Ebrahim, Per Olav Vandvik, Bradley C. Johnston, Martin A. Walter, Bernard Burnand, Matthias Schwenkglenks, Lars G. Hemkens, Maureen O. Meade, Heiner C. Bucher, Benjamin Kasenda, Matthias Briel

Bibliographic record

VenueCritical Care Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsHospital for Sick ChildrenCentre Hospitalier Universitaire de SherbrookeUniversité de SherbrookeUniversity of TorontoMcMaster University
FundersHamilton Health Sciences FoundationHamilton Health SciencesAcademy of FinlandSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche ForschungsgemeinschaftUniversitätsspital BaselSuomen KulttuurirahastoCanadian Institutes of Health ResearchNational Science FoundationSuomen Lääketieteen SäätiöUniversität ZürichJane ja Aatos Erkon SäätiöFondation BrocherF. Hoffmann-La Roche
KeywordsMedicineRandomized controlled trialDiscontinuationClinical trialAcute careEmergency departmentOdds ratioEmergency medicineIntensive careIntensive care medicineInternal medicinePhysical therapyHealth careNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Randomized clinical trials that enroll patients in critical or emergency care (acute care) setting are challenging because of narrow time windows for recruitment and the inability of many patients to provide informed consent. To assess the extent that recruitment challenges lead to randomized clinical trial discontinuation, we compared the discontinuation of acute care and nonacute care randomized clinical trials. DESIGN: Retrospective cohort of 894 randomized clinical trials approved by six institutional review boards in Switzerland, Germany, and Canada between 2000 and 2003. SETTING: Randomized clinical trials involving patients in an acute or nonacute care setting. SUBJECTS AND INTERVENTIONS: We recorded trial characteristics, self-reported trial discontinuation, and self-reported reasons for discontinuation from protocols, corresponding publications, institutional review board files, and a survey of investigators. MEASUREMENTS AND MAIN RESULTS: Of 894 randomized clinical trials, 64 (7%) were acute care randomized clinical trials (29 critical care and 35 emergency care). Compared with the 830 nonacute care randomized clinical trials, acute care randomized clinical trials were more frequently discontinued (28 of 64, 44% vs 221 of 830, 27%; p = 0.004). Slow recruitment was the most frequent reason for discontinuation, both in acute care (13 of 64, 20%) and in nonacute care randomized clinical trials (7 of 64, 11%). Logistic regression analyses suggested the acute care setting as an independent risk factor for randomized clinical trial discontinuation specifically as a result of slow recruitment (odds ratio, 4.00; 95% CI, 1.72-9.31) after adjusting for other established risk factors, including nonindustry sponsorship and small sample size. CONCLUSIONS: Acute care randomized clinical trials are more vulnerable to premature discontinuation than nonacute care randomized clinical trials and have an approximately four-fold higher risk of discontinuation due to slow recruitment. These results highlight the need for strategies to reliably prevent and resolve slow patient recruitment in randomized clinical trials conducted in the critical and emergency care setting.

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)0.019
metaresearch head score (Gemma)0.784
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.784
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.550
GPT teacher head0.646
Teacher spread0.096 · 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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations33
Published2015
Admission routes3
Has abstractyes

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