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Temporal trends in the inclusion of patient-reported outcomes in heart failure randomized trials published in high-impact medical journals: a systematic bibliometric review

2021· article· en· W4386661486 on OpenAlexafffundabout
Yousif Eliya, Tauben Averbuch, Nhu D. Le, Feng Xie, Lehana Thabane, Mamas A. Mamas, Harriette G.C. Van Spall

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityImpact
FundersCanadian Institutes of Health Research
KeywordsMedicineRandomized controlled trialOdds ratioConfidence intervalCINAHLMEDLINEInternal medicineClinical endpointPsychological interventionNursing

Abstract

fetched live from OpenAlex

Abstract Background Patient reported outcomes (PROs) are important measures of health, particularly in symptomatic conditions such as heart failure (HF), and regulatory agencies have encouraged their collection in randomized controlled trials (RCTs). Purpose To assess temporal trends in the inclusion of patient-reported outcomes (PROs) in heart failure (HF) randomized controlled trials (RCTs) published in high-impact medical journals, explore trial characteristics associated with inclusion of PROs, and describe the quality of PRO reporting in HF RCTs. Methods We searched MEDLINE, EMBASE and CINAHL for studies published between January 1, 2000 and July 17, 2020. We included RCTs published in journals with impact factor ≥10. We assessed temporal trends using the Jonckheere-Terpstra test and conducted multivariable logistic regression analysis to explore trial characteristics independently associated with inclusion of PROs. We assessed the quality of PRO reporting using the Consolidated Standards for Trial Reporting PRO extension statement. Results We identified 12,342 articles, of which 417 RCTs met inclusion criteria. PROs were included in 224 RCTs (53.7%, 95% confidence interval [CI]: 48.8%-58.6%), of which 44 (19.6%) reported PRO as primary or co-primary endpoint. The proportion of RCTs with PROs increased significantly between 2000–2003 (37.4%) and 2016–2020 (65.1%) (p<0.001). PROs had higher odds of inclusion in RCTs that were multicenter (odds ratio [OR]: 1.95; 95% CI: 1.05–3.64; p=0.036); medium-sized (n=51–250) (OR: 2.29; 95% CI: 1.24–4.23; p=0.008); coordinated in in Central and South America (OR: 6.79; 95% CI: 1.34–34.36; p=0.021); and assessed health services (OR: 4.21; 1.97–8.98; p<0.001), device / surgical (OR: 6.24; 95% CI 3.05–12.80; p<0.001), or exercise and rehabilitation interventions (OR: 3.98; 95% CI 1.59–9.97; p=0.003). A majority (54.9%) of the 224 RCTs reported four or less of the eleven CONSORT-PRO items. The median number of CONSORT-PRO items reported was 4 (interquartile range [IQR] 3–6 items per trial), with improved reporting in trials with PRO as primary or co-primary endpoint. Conclusions PROs are included in in just over half of HF RCTs, with an increased reporting of PROs between 2000 and 2020. Large, pharmacotherapy trials conducted in North America and Europe were less likely to include PROs. The quality of PRO reporting in HF RCTs was modest, with trials most often reporting four of eleven CONSORT-PRO items. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research

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: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
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)0.379
metaresearch head score (Gemma)0.224
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3790.224
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0220.020
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.376
GPT teacher head0.490
Teacher spread0.114 · 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 designSystematic review · Observational
DomainReporting · Methods
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
Published2021
Admission routes3
Has abstractyes

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