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Record W4255677283 · doi:10.1093/ndt/gfaa008

Identifying critically important cardiovascular outcomes for trials in hemodialysis: an international survey with patients, caregivers and health professionals

2020· article· en· W4255677283 on OpenAlexaff
Emma O’Lone, Martin Howell, Andrea K. Viecelli, Jonathan C. Craig, Allison Tong, Bénédicte Sautenet, William G. Herrington, Charles A. Herzog, Tazeen H. Jafar, Meg Jardine, Vera Krane, Adeera Levin, Jolanta Małyszko, Michael V. Rocco, Giovanni FM Strippoli, Marcello Tonelli, Angela Yee‐Moon Wang, Christoph Wanner, Faı̈ez Zannad, Wolfgang C. Winkelmayer­, David C. Wheeler

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersNational Health and Medical Research CouncilMedical Research CouncilKidney Research UK
KeywordsLikert scaleMedicineClinical equipoiseClinical trialFamily medicinePhysical therapyInternal medicinePsychology

Abstract

fetched live from OpenAlex

Abstract Background Cardiovascular disease (CVD) is a major contributor to morbidity and mortality in people on hemodialysis (HD). Cardiovascular outcomes are reported infrequently and inconsistently across trials in HD. This study aimed to identify the priorities of patients/caregivers and health professionals (HPs) for CVD outcomes to be incorporated into a core outcome set reported in all HD trials. Methods In an international online survey, participants rated the absolute importance of 10 cardiovascular outcomes (derived from a systematic review) on a 9-point Likert scale, with 7–9 being critically important. The relative importance was determined using a best–worst scale. Likert means, medians and proportions and best–worst preference scores were calculated for each outcome. Comments were thematically analyzed. Results Participants included 127 (19%) patients/caregivers and 549 (81%) HPs from 53 countries, of whom 530 (78%) completed the survey in English and 146 (22%) in Chinese. All but one cardiovascular outcome (‘valve replacement’) was rated as critically important (Likert 7–9) by all participants; ‘sudden cardiac death’, ‘heart attack’, ‘stroke’ and ‘heart failure’ were all rated at the top by patients/caregivers (median Likert score 9). Patients/caregivers ranked the same four outcomes as the most important outcomes with mean preference scores of 6.2 (95% confidence interval 4.8–7.5), 5.9 (4.6–7.2), 5.3 (4.0–6.6) and 4.9 (3.6–6.3), respectively. The same four outcomes were ranked most highly by HPs. We identified five themes underpinning the prioritization of outcomes: ‘clinical equipoise and potential for intervention’, ‘specific or attributable to HD’, ‘severity or impact on the quality of life’, ‘strengthen knowledge and education’, and ‘inextricably linked burden and risk’. Conclusions Patients and HPs believe that all cardiovascular outcomes are of critical importance but consistently identify sudden cardiac death, myocardial infarction, stroke and heart failure as the most important outcomes to be measured in all HD trials.

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)0.035
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.346
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations12
Published2020
Admission routes1
Has abstractyes

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