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Record W4285091099 · doi:10.1503/cjs.001121

Mechanical versus bioprosthetic valves in chronic dialysis: a systematic review and meta-analysis

2022· review· en· W4285091099 on OpenAlexafffundvenue
Kevin Kim, Emilie P. Belley‐Côté, Saurabh Gupta, Arjun Pandey, Ali Alsagheir, Ahmad Makhdoum, G. McClure, Brooke Newsome, Sophie Gao, Matthias Bossard, Tetsuya Isayama, Yasuhisa Ikuta, Michael Walsh, Amit X. Garg, Gordon Guyatt, Richard Whitlock

Bibliographic record

VenueCanadian Journal of Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineCholineInternal medicineGastroenterologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Many patients with end-stage kidney disease (ESKD) have valvular heart disease requiring surgery. The optimal prosthetic valve is not established in this population. We performed a systematic review and meta-analysis to assess outcomes of patients with dialysis-dependent ESKD who received mechanical or bioprosthetic valves. METHODS: We searched Cochrane Central, Medline and Embase from inception to January 2020. We performed screening, full-text assessment, risk of bias and data collection, independently and in duplicate. Data were pooled using a random-effects model. RESULTS: = 0%, absolute effect 25 more events per 1000). CONCLUSION: Mechanical valves were associated with reduced mortality, but increased rate of bleeding and stroke. Given very low certainty for evidence of mortality and stroke outcomes, patients and clinicians may choose prosthetic valves based on factors such as bleeding risk and valve longevity. STUDY REGISTRATION: PROSPERO no. CRD42017081863.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0140.024
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.161
GPT teacher head0.387
Teacher spread0.225 · 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; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
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

Citations19
Published2022
Admission routes3
Has abstractyes

Explore more

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