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Record W157863262

Screening and therapy for malnutrition and related gastro-intestinal disorders in systemic sclerosis: recommendations of a North American expert panel.

2010· article· en· W157863262 on OpenAlexaffabout
Murray Baron, Paule Bernier, Louis-François Côté, Mark H. DeLegge, Glenda Falovitch, Gad Friedman, Mervyn Gornitsky, John Hoffer, Marie Hudson, Dinesh Khanna, William G. Paterson, Donna Schafer, Phillip P. Toskes, Linda Wykes

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineMalnutritionReferralMalabsorptionScleroderma (fungus)Intensive care medicineMedical nutrition therapyFamily medicinePhysical therapyPediatricsInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To develop a set of recommendations for clinicians caring for patients with systemic sclerosis (SSc) to guide their approach to the patient with malnutrition and possible malabsorption. METHODS: The Canadian Scleroderma Research Group convened a meeting of experts in the areas of nutrition, speech pathology, oral health in SSc, SSc and gastroenterology to discuss the nutrition-GI paradigm in SSc. This meeting generated a set of recommendations based on expert opinion. RESULTS: Physicians should screen ALL patients with SSc for malnutrition. The physician should ask a series of questions that pertain to GI involvement. Patients who screen positive for malnutrition should be referred to a dietitian and gastroenterologist. Referral to a patient support group should be considered and if screening reveals oral health problems, referral to a dentist, preferably with expertise in treating patients with SSc, should be done. All SSc patients should weigh themselves monthly and report any sudden significant changes in weight. They should be assessed by a rheumatologist once a year for signs of malnutrition. CONCLUSIONS: Malnutrition may be common in SSc and a multidisciplinary approach is important.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.482
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.038
GPT teacher head0.249
Teacher spread0.211 · 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

Citations66
Published2010
Admission routes2
Has abstractyes

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