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Symptomatic fracture incidence in those under 50 years of age in southern Tasmania

2002· article· en· W1997584511 on OpenAlexfundno aff
Graeme Jones, HM Cooley

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2002
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
FundersMcGill UniversityArthritis Foundation of AustraliaMerck Sharp and DohmeArthritis Foundation
KeywordsMedicineIncidence (geometry)OsteoporosisPopulationForearmPediatricsDemographyWristSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To document symptomatic fracture incidence in those aged under 50 years of age. METHODS: Fractures were ascertained from X-ray reports containing the word 'fracture' from all radiology providers for the geographically defined population of southern Tasmania (n = 165 175) for the period 1 July 1997 to 30 June 1999. RESULTS: In the 2-year study frame there were 2943 fractures in 164 730 person years in males and 1348 fractures in 165 620 person years in females. This represents a fracture incidence of 1787 per 100 000 person years in males and 819 per 100 000 person years in females. Peak fracture incidence was 10-14 years in females and 15-19 years in males although different fracture types had varying peak incidence suggesting different fracture-specific causes. The most common fractures were those of the hand (24%), forearm (17%), wrist (10%) and foot (9%). All fractures (including vertebral) were more common in males with relative risks ranging from 1.34 to 4.50. The estimated probability of at least one fracture between birth and 50 years of age was 59% for males and 34% for females. CONCLUSION: There are threefold as many fractures in this age group compared to those due to osteoporosis in the elderly in any given year. More research priority needs to be given to understanding the causes of these fractures so that preventive strategies can be formulated.

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)0.001
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 score0.042
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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

Citations69
Published2002
Admission routes1
Has abstractyes

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