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Record W2112658951 · doi:10.1136/bmjopen-2013-003436

Reporting of harms data in RCTs: a systematic review of empirical assessments against the CONSORT harms extension

2013· review· en· W2112658951 on OpenAlexfundno aff
Alexander Hodkinson, Jamie J Kirkham, Catrin Tudur Smith, Carrol Gamble

Bibliographic record

VenueBMJ Open · 2013
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
FundersMedical Research CouncilYork University
KeywordsMedicineConsolidated Standards of Reporting TrialsAlternative medicineSystematic reviewTraditional medicineFamily medicineMEDLINEEnvironmental healthPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the standard of reporting of harms-related data, in randomised controlled trials (RCTs) according to the Consolidated Standards of Reporting Trials (CONSORT) statement extension for harms. DESIGN: Systematic review. DATA SOURCES: The Cochrane library, Ovid MEDLINE, Scopus and ISI Web of Knowledge were searched for relevant literature. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included publications of studies that used the CONSORT harms extension to assess the reporting of harms in RCTs. RESULTS: We identified 7 studies which included between 10 and 205 RCTs. The clinical areas of the 7 studies were: hypertension (1), urology (1), epilepsy (1), complimentary medicine (2) and two not restricted to a clinical topic. Quality of the 7 studies was assessed by a risk of bias tool and was found to be variable. Adherence to the CONSORT harms criteria reported in the 7 studies was inadequate and variable across the items in the checklist. Adverse events are poorly defined, with 6 studies failing to exceed 50% adherence to the items in the checklist. CONCLUSIONS: Readers of RCT publications need to be able to balance the trade-offs between benefits and harms of interventions. This systematic review suggests that this is compromised due to poor reporting of harms which is evident across a range of clinical areas. Improvements in quality could be achieved by wider adoption of the CONSORT harms criteria by journals reporting RCTs.

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)0.559
metaresearch head score (Gemma)0.421
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5590.421
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0430.004
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0180.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.965
GPT teacher head0.738
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
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

Citations121
Published2013
Admission routes1
Has abstractyes

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