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Record W3095291465 · doi:10.14740/ijcp403

Insights Into Management of Camurati-Engelmann Disease: A Case Series of Three Siblings

2020· article· en· W3095291465 on OpenAlexvenueno aff
W. Hunter Slemp, Janel D. Hunter, Elizabeth T. Walsh, Cathrine Constantacos, David F. Crudo

Bibliographic record

VenueInternational Journal of Clinical Pediatrics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDermatological and Skeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDeflazacortMedicinePrednisoneLosartanInternal medicinePhysical therapyPediatricsAngiotensin II

Abstract

fetched live from OpenAlex

Camurati-Engelmann disease (CED) is an autosomal dominant skeletal dysplasia characterized by progressive sclerosis of long bones due to a mutation in the transforming growth factor beta-1 gene. Patients experience progressive pain, weakness, and fatigability over time. There are no consensus guidelines for treatment though the use of several types of glucocorticoids, angiotensin II receptor blockers, and other therapies have been described. We present the cases of three siblings with CED managed with different treatment modalities over time (prednisone, losartan, and deflazacort). We provide objective data (pain scores, walk-test results, erythrocyte sedimentation rates) to demonstrate therapeutic efficacy. Prednisone resulted in the greatest improvement in pain; however, its use was limited by significant weight gain. Deflazacort was successful in improving pain and fatigability without the weight gain. Risks and benefits must be considered carefully as the cost of deflazacort is significantly higher than prednisone or losartan. Int J Clin Pediatr. 2020;9(4):130-134 doi: https://doi.org/10.14740/ijcp403

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)0.001
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 score0.333
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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