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Record W2901929264 · doi:10.3899/jrheum.171445

Relationship Between Increased Fecal Calprotectin Levels and Interstitial Lung Disease in Systemic Sclerosis

2018· article· en· W2901929264 on OpenAlexvenueno aff
Cristian Caimmi, Eugenia Bertoldo, Anna P. Venturini, Paola Caramaschi, Luca Frulloni, Rachele Ciccocioppo, S. Brunelli, Luca Idolazzi, Davide Gatti, Maurizio Rossini, Ombretta Viapiana

Bibliographic record

VenueThe Journal of Rheumatology · 2018
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCalprotectinInterstitial lung diseaseInternal medicineGastroenterologyLungMultivariate analysisScleroderma (fungus)DiverticulosisSystemic diseaseFecesDiseasePathologyInflammatory bowel disease

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the relationship between fecal calprotectin (FC) and interstitial lung disease (ILD) in systemic sclerosis (SSc). METHODS: The study enrolled 129 outpatients with SSc. Data about disease characteristics, in particular lung involvement, were collected and FC was measured. RESULTS: Patients with ILD (35, 27.1%) had higher values of FC (p < 0.001). In multivariate analysis, these variables were associated with increased risk of ILD: diffuse disease subset, higher modified Rodnan skin score, longer disease duration, higher severity scores, steroid treatment, and higher FC levels, while diverticulosis was protective. CONCLUSION: ILD is independently associated with increased FC levels in 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 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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.053
GPT teacher head0.293
Teacher spread0.240 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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