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Record W7073951682

Vitamin D and lung function decline in adults with asthma: the HUNT study

2016· article· en· W7073951682 on OpenAlexfundno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2016
Typearticle
Languageen
FieldEngineering
TopicMuon and positron interactions and applications
Canadian institutionsnot available
FundersNorges Teknisk-Naturvitenskapelige UniversitetUniversity of OttawaMassachusetts General Hospital
KeywordsLung functionVital capacityAsthmaVitamin D and neurologyPulmonary function testingLungLung volumes
DOInot available

Abstract

fetched live from OpenAlex

We investigated whether low 25-hydroxyvitamin D (25(OH)D) levels were associated with more lung function decline in adults with asthma and whether this association was modified by smoking status or inhaled corticosteroid (ICS) use. We analyzed data on 395 adults with asthma from the Nord-Trøndelag Health Study (1995–2008), Norway. Plasma 25(OH)D and lung function were measured at baseline, and lung function measurements were repeated at follow-up, approximately 11 years later. Linear regression was used to estimate lung function decline. Participants with low 25(OH)D (<50 nmol/L) had more decline in lung function measurements for forced expiratory volume in 1 second (FEV1) (388 mL), forced vital capacity (298 mL), and the FEV1/forced vital capacity ratio (3.7%) over the follow-up, compared with those with high 25(OH)D (≥50 nmol/L) who declined 314 mL, 246 mL, and 3.0%, respectively (P = 0.08, 0.30, and 0.23, respectively). The associations were stronger in never smokers and non-ICS users. In never smokers, low 25(OH)D levels were associated with more decline in FEV1 (445 vs. 222 mL) (P = 0.01). In non-ICS users, low 25(OH)D levels were associated with more decline in FEV1 (467 vs. 320 mL) (P = 0.02). Low serum 25(OH)D levels were weakly associated with more lung function decline in adults with asthma, and stronger associations were observed in never smokers and non-ICS users.

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 score0.643
Threshold uncertainty score0.343

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)0.0000.000

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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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
Published2016
Admission routes1
Has abstractyes

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