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Record W2091368944 · doi:10.1080/07853890510007250

Osteoporosis in chronically ill children

2005· article· en· W2091368944 on OpenAlexaff
Etienne Sochett, Outi Mäkitie

Bibliographic record

VenueAnnals of Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineOsteoporosisPeak bone massVitamin D and neurologyDensitometryBone diseasePediatricsBone remodelingBone massMetabolic bone diseaseBone densityOsteopeniaBone mineralInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Gains in bone mass are very rapid during adolescence and peak bone mass, the most important determinant of osteoporosis, is attained by early adulthood. Glucocorticoids, widely used in children with chronic illness, are known to impact bone mass and quality. In addition, disease and treatment-related factors, nutrient and hormone deficiencies and decreased physical activity may all negatively affect bone mass accrual. Although decreased bone density is increasingly recognized in chronically ill children, current knowledge of the epidemiology, diagnosis and optimal treatment of pediatric secondary osteoporosis is limited. In addition to bone densitometry, biochemical and radiographic tests should be used in the diagnosis of osteoporosis. Bone histomorphometry may be needed in selected situations. At risk children should be advised to ensure sufficient calcium and vitamin D intake and weight bearing physical activity. Growth and pubertal development require careful assessment because of their close correlation with bone formation. Given limited experience with bisphosphonates, it seems prudent to target antiresorptive therapy to those children who have developed symptomatic disease. Ideally this should be done in controlled settings. Early identification and adequate intervention, in selected cases with bisphosphonates, is needed in order to prevent deleterious skeletal complications of osteoporosis in chronically ill children.

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 score0.351
Threshold uncertainty score0.867

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)0.0010.000

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 head0.035
GPT teacher head0.345
Teacher spread0.310 · 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

Citations25
Published2005
Admission routes1
Has abstractyes

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