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Record W2334295167 · doi:10.1097/bor.0000000000000027

Mortality in systemic sclerosis

2014· review· en· W2334295167 on OpenAlexaff
Mandana Nikpour, Murray Baron

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2014
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicineLife expectancyDiseaseMortality rateHazard ratioPopulationInterstitial lung diseaseIntensive care medicineInternal medicineCause of deathLungConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Systemic sclerosis (SSc) has a case-based mortality that is one of the highest among the rheumatic diseases. This article is an appraisal of current knowledge regarding survival, causes of death and risk factors for reduced life-expectancy in systemic sclerosis (SSc). RECENT FINDINGS: Recent systematic reviews of cohorts studies published worldwide have revealed a pooled standardized mortality ratio in SSc of 3.5, and reiterated the importance of heart-lung involvement as a major cause of death in this disease. Indeed, the pooled hazard ratio (HR) of mortality in SSc patients with pulmonary arterial hypertension (PAH) compared with those without is 3.5, while the pooled HR for mortality in those with interstitial lung disease is 2.6. The average life expectancy of patients with SSc is 16-34 years less than age-matched and sex-matched population peers. Current research efforts are focused on quantifying early as well as late mortality, and modeling for predictors of death in SSc, with the ultimate goal of attenuating this risk and improving survival, as new therapies emerge. SUMMARY: Studies have consistently shown a substantially increased mortality in SSc, predominantly due to cardio-pulmonary complications. A better understanding of risk factors for mortality holds the promise of improving outcomes in this devastating multiorgan autoimmune disease.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.196
GPT teacher head0.422
Teacher spread0.226 · 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 designSystematic review
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

Citations85
Published2014
Admission routes1
Has abstractyes

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