MétaCan
Menu
Back to cohort
Record W4389329638 · doi:10.1159/000535558

A Systematic Bibliometric Analysis of High-Impact Articles in Critical Care Nephrology

2023· review· en· W4389329638 on OpenAlex
Jaye M. Platnich, Janice Y. Kung, Adam Romanovsky, Marlies Ostermann, Ron Wald, Neesh Pannu, Sean M. Bagshaw

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBlood Purification · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of TorontoSt. Michael's HospitalAlberta HealthUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsImpact factorSubspecialtyMedicineBibliometricsMEDLINEInternal medicineWeb of scienceAdjudicationFamily medicineMeta-analysisLibrary sciencePolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Critical care nephrology is a subspecialty that merges critical care and nephrology in response to shared pathobiology, clinical care, and technological innovations. To date, there has been no description of the highest impact articles. Accordingly, we systematically identified high impact articles in critical care nephrology. METHODS: This was a bibliometric analysis. The search was developed by a research librarian. Web of Science was searched for articles published between January 1, 2000 and December 31, 2020. Articles required a minimum of 30 citations, publication in English language, and reporting of primary (or secondary) original data. Articles were screened by two reviewers for eligibility and further adjudicated by three experts. The "Top 100" articles were hierarchically ranked by adjudication, citations in the 2 years following publication and journal impact factor (IF). For each article, we extracted detailed bibliometric data. Risk of bias was assessed for randomized trials by the Cochrane Risk of Bias tool. Analyses were descriptive. RESULTS: The search yielded 2,805 articles. Following initial screening, 307 articles were selected for full review and adjudication. The Top 100 articles were published across 20 journals (median [IQR] IF 10.6 [8.9-56.3]), 38% were published in the 5 years ending in 2020 and 62% were open access. The agreement between adjudicators was excellent (intraclass correlation, 0.96; 95% CI, 0.84-0.99). Of the Top 100, 44% were randomized trials, 35% were observational, 14% were systematic reviews, 6% were nonrandomized interventional studies and one article was a consensus document. The risk of bias among randomized trials was low. Common subgroup themes were RRT (42%), AKI (30%), fluids/resuscitation (14%), pediatrics (10%), interventions (8%), and perioperative care (6%). The citations for the Top 100 articles were 175 (95-393) and 9 were cited >1,000 times. CONCLUSION: Critical care nephrology has matured as an important subspecialty of critical care and nephrology. These high impact papers have focused largely on original studies, mostly clinical trials, within a few core themes. This list can be leveraged for curricula development, to stimulate research, and for quality assurance.

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.

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)0.077
metaresearch head score (Gemma)0.115
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0770.115
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0210.008
Bibliometrics0.2130.479
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.745
GPT teacher head0.586
Teacher spread0.158 · 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