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Bones and Crohnʼs

2004· article· en· W2122486133 on OpenAlexafffund
Jesse Siffledeen, Richard N. Fedorak, Kerry Siminoski, Ho Jen, Eric Vaudan, Neena S. Abraham, Hillary Seinhart, Gordon R. Greenberg

Bibliographic record

VenueInflammatory Bowel Diseases · 2004
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersCrohn's and Colitis Foundation of CanadaCrohn's and Colitis Foundation
KeywordsOsteopeniaBone mineralMedicineOsteoporosisFemoral neckBone densityInternal medicineBone diseaseOutpatient clinicGastroenterology

Abstract

fetched live from OpenAlex

Previous studies have confirmed that the prevalence of decreased bone mineral density is elevated in patients with inflammatory bowel disease. The objective of the current study was to determine the prevalence of osteopenia and osteoporosis in a cross-sectional outpatient population of 242 adult patients with Crohn's disease and to determine which clinical characteristics and serum and urine biochemical factors might be predictive of bone loss. Thirty-seven percent had normal bone density, 50.0% were osteopenic, and 12.9% were osteoporotic. Among the sites used to diagnose low bone mineral density, the femoral neck demonstrated the highest prevalence of osteopenia and the ultra-distal radius the highest prevalence of osteoporosis. However, low bone mineral density at one site was always predictive of low bone mineral density at the other. Corticosteroid use during the year before assessment was found to be consistently predictive of low bone mineral density in males but not in females. In contrast, low body mass index and high platelet counts were consistently predictive of low bone mineral density in females but not in males. Disease location, smoking, and age were not predictive of changes in bone mineral density.

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 score0.007
Threshold uncertainty score0.427

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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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

Citations77
Published2004
Admission routes2
Has abstractyes

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