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Record W2348056899 · doi:10.1371/journal.pone.0155078

Quality of Reporting and Study Design of CKD Cohort Studies Assessing Mortality in the Elderly Before and After STROBE: A Systematic Review

2016· review· en· W2348056899 on OpenAlexaboutno aff
Anirudh Rao, Katharina Brück, Shona Methven, Rebecca Evans, Vianda S Stel, Kitty J. Jager, Lotty Hooft, Yoav Ben‐Shlomo, Fergus Caskey

Bibliographic record

VenuePLoS ONE · 2016
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersEuropean Renal Association-European Dialysis and Transplant AssociationUniversiteit van AmsterdamNational Institute for Health and Care Research
KeywordsStrengthening the reporting of observational studies in epidemiologyObservational studyMedicineCritical appraisalChecklistMEDLINESystematic reviewCohort studyPopulationClinical study designResearch designFamily medicineQuartileQuality ScoreInternal medicineClinical trialEnvironmental healthAlternative medicinePsychologyConfidence intervalPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement was published in October 2007 to improve quality of reporting of observational studies. The aim of this review was to assess the impact of the STROBE statement on observational study reporting and study design quality in the nephrology literature. STUDY DESIGN: Systematic literature review. SETTING & POPULATION: European and North American, Pre-dialysis Chronic Kidney Disease (CKD) cohort studies. SELECTION CRITERIA FOR STUDIES: Studies assessing the association between CKD and mortality in the elderly (>65 years) published from 1st January 2002 to 31st December 2013 were included, following systematic searching of MEDLINE & EMBASE. PREDICTOR: Time period before and after the publication of the STROBE statement. OUTCOME: Quality of study reporting using the STROBE statement and quality of study design using the Newcastle Ottawa Scale (NOS), Scottish Intercollegiate Guidelines Network (SIGN) and Critical Appraisal Skills Programme (CASP) tools. RESULTS: 37 papers (11 Pre & 26 Post STROBE) were identified from 3621 potential articles. Only four of the 22 STROBE items and their sub-criteria (objectives reporting, choice of quantitative groups and description of and carrying out sensitivity analysis) showed improvements, with the majority of items showing little change between the period before and after publication of the STROBE statement. Pre- and post-period analysis revealed a Manuscript STROBE score increase (median score 77.8% (Inter-quartile range [IQR], 64.7-82.0) vs 83% (IQR, 78.4-84.9, p = 0.05). There was no change in quality of study design with identical median scores in the two periods for NOS (Manuscript NOS score 88.9), SIGN (Manuscript SIGN score 83.3) and CASP (Manuscript CASP score 91.7) tools. LIMITATIONS: Only 37 Studies from Europe and North America were included from one medical specialty. Assessment of study design largely reliant on good reporting. CONCLUSIONS: This study highlights continuing deficiencies in the reporting of STROBE items and their sub-criteria in cohort studies in nephrology. There was weak evidence of improvement in the overall reporting quality, with no improvement in methodological quality of CKD cohort studies between the period before and after publication of the STROBE statement.

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)0.386
metaresearch head score (Gemma)0.690
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.614
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3860.690
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0140.018
Bibliometrics0.0190.022
Science and technology studies0.0030.006
Scholarly communication0.0100.010
Open science0.0060.006
Research integrity0.0060.004
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 head0.276
GPT teacher head0.441
Teacher spread0.166 · 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 designSystematic review
DomainReporting
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

Citations41
Published2016
Admission routes1
Has abstractyes

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