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Record W2320350537 · doi:10.1097/ccm.0000000000001466

Determinants of Citation Impact in Large Clinical Trials in Critical Care

2015· review· en· W2320350537 on OpenAlexaff
John C. Marshall, Wilson Kwong, Kamya Kommaraju, Karen E. A. Burns

Bibliographic record

VenueCritical Care Medicine · 2015
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's UniversitySt. Michael's Hospital
Fundersnot available
KeywordsMedicineInterquartile rangeClinical trialRandomized controlled trialMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Randomized clinical trials provide the best evidence of treatment effectiveness; factors determining their impact are unknown. We sought to determine the influence of funding (industry vs nonindustry), research (comparative effectiveness vs technology evaluation), and organizational models (investigator-led trials group vs others) on the impact of large trials in critical care medicine. DATA SOURCES: We searched MEDLINE for randomized clinical trials published between 1990 and 2012 in five critical care, five general interest, and one pediatrics journal. Impact was evaluated as annual citation rates measured using the ISI Web of Knowledge database. STUDY SELECTION: Eligible trials enrolled at least 100 critically ill adults, children, or neonates, evaluated an intervention that was applied during the ICU stay, and reported mortality and/or length of ICU or hospital stay. DATA EXTRACTION: Two reviewers identified eligible studies, and two separate reviewers extracted data. DATA SYNTHESIS: We identified 391 randomized clinical trials, recruiting 208,154 subjects. Funding source--industry versus peer review versus mixed--did not impact citation rates. Comparative effectiveness studies made up 52.5% of the reports and were cited more frequently than studies evaluating novel technologies (median, 15.6 vs 10.3 citations/yr; p = 0.002). Trials conducted by investigator-led trials groups (n = 45) were cited a median of 45.7 (interquartile range [IQR], 17.3-86.2) times per year, significantly more often (p < 0.0001) than multicenter trials conducted by ad hoc groups (n = 89; median, 19 [IQR, 8.7-30.4]) or industry (n = 85; median, 12.3 [IQR, 5.4-24.1]), and more than single-center trials (n = 116; median, 6.8 [IQR, 3.5-12.8]) or small ad hoc trials involving two to five centers (n = 59; median, 11.0 [IQR, 4.5-22.4]). Although only 11.5% of all trials included, randomized clinical trials from investigator-led research consortia accounted for nine of the 16 studies cited more than 100 times per year and 23.4% of all citations; their costs were substantially less than the typical costs of industry-run trials.. CONCLUSIONS: Clinical trials conducted by investigator-led research groups are significantly more frequently cited than industry-led trials in critical care medicine. In addition, costs appear to be substantially lower with investigator-led trials. Support for and expansion of this model of research can ensure that critical care research is clinically relevant and practice changing.

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

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
gemmaMetaresearchBibliometrics
Domain: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationallow
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)0.427
metaresearch head score (Gemma)0.881
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4270.881
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0420.006
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.947
GPT teacher head0.769
Teacher spread0.178 · 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

Labeled directly by 2 models reading the full record.

MetaresearchBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainEvaluation
GenreReview

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

Citations14
Published2015
Admission routes1
Has abstractyes

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