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Record W3062242342 · doi:10.1038/s41533-020-00194-9

Impact of comorbid conditions on asthmatic adults and children

2020· review· en· W3062242342 on OpenAlexaff
Alan Kaplan, Stanley J. Szefler, David Halpin

Bibliographic record

Venuenpj Primary Care Respiratory Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsThornhill Medical (Canada)
FundersNatureBoehringer Ingelheim
KeywordsMedicineAsthmaComorbidityExacerbationObesityPediatricsIntensive care medicineVocal cord dysfunctionPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Comorbid conditions (comorbidities) can complicate the diagnosis and management of asthma. In different age groups, comorbid conditions can present varying challenges, including diagnostic confusion due to mimicking asthma symptoms, exacerbation of asthma symptoms, therapy for comorbid conditions affecting asthma or therapy for asthma affecting these conditions. This review aims to summarise some common comorbid conditions with asthma, such as rhinitis, vocal cord dysfunction, gastro-oesophageal reflux, psychiatric disorders, obesity and obstructive sleep apnoea, and discuss their prevalence, symptoms, diagnosis and treatment, highlighting any differences in how they impact children and adults. Overall, there is a lack of data on the impact of treating comorbid conditions on asthma outcomes and further studies are needed to guide age-appropriate asthma management in the presence of these conditions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.359
Teacher spread0.331 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations81
Published2020
Admission routes1
Has abstractyes

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