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Record W4390976545 · doi:10.1016/j.rpth.2024.102315

Primary prevention of venous thromboembolism for cancer patients in randomized controlled trials: a bibliographical analysis of funding and trial characteristics

2024· article· en· W4390976545 on OpenAlexaff
Lucy Zhao, Jayhan Kherani, Pei Ye Li, Kevin Zhang, Angelina Horta, Allen Li, Ali Eshaghpour, Mark Crowther

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2024
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of OttawaUniversity of TorontoMcMaster University
FundersBayerPfizer
KeywordsMedicineRandomized controlled trialProtocol (science)MEDLINEExact testClinical trialTest (biology)Data extractionPublication biasMeta-analysisFamily medicineInternal medicinePhysical therapyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background The majority of randomized controlled trials (RCTs) investigating venous thromboembolism (VTE) prophylaxis in patients with cancer involve commercial sponsorship. Commercial sponsorship overcomes feasibility limitations inherent in RCTs, such as recruitment and funding, but has attracted scrutiny for its potential for bias. Objectives In RCTs of VTE prophylaxis in patients with cancer, how do trial characteristics compare between commercially sponsored RCTs and noncommercially sponsored RCTs? Methods Medline, Embase, and Cochrane Central Register of Controlled Trials were searched for RCTs that investigated at least 1 pharmacologic intervention for VTE prophylaxis in adult patients with cancer. Screening and data extraction were conducted by independent reviewers. Outcomes included trial characteristics, reporting of favorable outcomes, protocol-manuscript discrepancies, and appraisal of spin. Outcomes were compared using the independent t -test, Mann–Whitney U-test, Pearson chi-squared test, and Fisher's exact test. Logistic regression was performed to identify factors associated with possible bias. Results Of the 54 trials analyzed, 34 (63%) reported commercial sponsorship. Commercial sponsorship was not associated with the reporting of favorable outcomes, presence of spin, retrospective registration, or protocol-manuscript discrepancy. Spin was most prevalent in the abstract conclusions (9 out of 17 [53.3%]) and manuscript conclusions (8 out of 17 [46.7%]). Commercially sponsored trials had a higher rate of intention-to-treat analysis. Noncommercially sponsored trials were more likely to report retrospective registration of trial protocol and the use of composite primary outcomes. Conclusion There were few significant differences between trial characteristics, suggesting that the evidence from commercially sponsored trials investigating VTE prophylaxis in patients with cancer is unlikely to be subject to bias attributable to commercial sponsorship.

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)0.226
metaresearch head score (Gemma)0.629
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2260.629
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0170.015
Bibliometrics0.0920.101
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0030.004
Research integrity0.0030.002
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 head0.204
GPT teacher head0.499
Teacher spread0.295 · 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 designObservational
DomainIncentives
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

Citations1
Published2024
Admission routes1
Has abstractyes

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