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Record W2025379530 · doi:10.1155/2010/347402

An International, Web-Based, Prospective Cohort Study to Determine Whether the Use of ACE Inhibitors prior to the Onset of Scleroderma Renal Crisis Is Associated with Worse Outcomes—Methodology and Preliminary Results

2010· article· en· W2025379530 on OpenAlexafffund
Marie Hudson, Murray Baron, Ernest Lo, Joanna Weinfeld, Daniel E. Furst, Dinesh Khanna

Bibliographic record

VenueInternational Journal of Rheumatology · 2010
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineProspective cohort studyCohortScleroderma (fungus)Cohort studyInternal medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

Background. To describe the methodology of a study designed to determine whether systemic sclerosis (SSc) patients with incident scleroderma renal crisis (SRC) on angiotensin converting enzyme (ACE) inhibitors prior to the onset of SRC have worse outcomes. Methods. Prospective, international cohort study of SRC subjects identified through an ongoing web-based survey. Every second Friday afternoon, an e-mail was sent to 589 participating physicians to identify new cases of SRC. Death or dialysis at one year after the onset of SRC will be compared in patients exposed or not to ACE inhibitors prior to the onset of SRC. Results. Fifteen months after the start of the survey, we had identified 76 incident cases of SRC. Of these, 66 (87%) had a hypertensive SRC and 10 (13%) a normotensive SRC. Twenty-two percent (22%) of the patients were on an ACE inhibitor immediately prior to the onset of the SRC. To date, we have collected one-year follow-up data on approximately 1/3 of the cohort. Of these, over 50% have died or remain on dialysis at one year. Conclusion. An international, web-based cohort study design is a feasible method of recruiting a substantial number of patients to study an infrequent vascular manifestation of SSc.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.333
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 teacher head, 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

Citations15
Published2010
Admission routes2
Has abstractyes

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