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The Prevalence of Three Prominent Corticosteroid Side Effects in a Large Asthma Population by Age, Sex and ICD-10 Asthma Severity

2024· preprint· en· W4399716916 on OpenAlexaff
Bowen Yao, Ruitao Zou, Thomas E. Wilson, Nicholas Orfan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsImpactUniversity of Toronto
Fundersnot available
KeywordsAsthmaMedicineOsteoporosisCataractsCorticosteroidPopulationPediatricsAdverse effectInternal medicineEpidemiologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: The adverse effects of corticosteroid therapy in the treatment of asthma have been extensively documented and explored over the past several decades. Prominent among these adverse effects are osteoporosis, cataracts and osteonecrosis. We assessed the prevalence of these three well known side effects of corticosteroid therapy for asthma in asthmatics versus non asthmatics from a general adult population of 3.5 million individuals. Methods: Using the Colorado all payers claims database with dates of service January 1, 2017 through June 30, 2020, we determined the prevalence of osteoporosis, cataracts and osteonecrosis by asthma severity, age and sex as compared with an age and sex matched non asthmatic comparator group. Results: Asthmatics generally showed an earlier onset and higher prevalence of these three side effects which correlated with asthma severity, often reaching statistically significant divergence from the non asthmatic comparator group. Patterns of prevalence with regard to both age and sex were distinctive for each side effect. Conclusion: Based on our findings, we suggest customized screening guidelines for osteoporosis, cataracts and osteonecrosis for specific sub-populations of asthmatics as defined by age, sex and asthma severity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.751

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 science0.0000.001
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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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