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Record W2136884830 · doi:10.1183/09031936.00120407

Comparative airway response to high-<i>versus</i>low-molecular weight agents in occupational asthma

2009· article· en· W2136884830 on OpenAlexafffund
M-H. Dufour, Catherine Lemière, Patricia Prince, LP Boulet

Bibliographic record

VenueEuropean Respiratory Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsUniversité LavalHôpital du Sacré-Cœur de MontréalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsAirwayAsthmaOccupational asthmaMedicineInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Airway responses to occupational agents in sensitised workers may vary clinically and physiologically. The patterns of change in airway responsiveness, type of response and fall in expiratory flows following laboratory exposure to high- or low-molecular weight agents (HMW and LMW agents, respectively) were compared in sensitised workers. Data on workers who underwent specific inhalation challenges with occupational sensitisers (117 exposed to HMW agents and 130 to LMW agents) were collected from their medical charts. Maximum falls in forced expiratory volume in one second (FEV(1)) were of similar magnitude for both types of agents. Compared with HMW agents, LMW agents induced more frequently late or dual responses and higher increases in airway responsiveness. After exposure to HMW agents, there was a mean+/-sd reduction in doubling concentrations of methacholine of 0.5+/-1.7 for early responses, compared with 2.8+/-1.2 and 1.4+/-2.0 for late and dual responses, respectively. Isolated early responses were more frequently found in females, smokers, workers with a higher % predicted FEV(1) and higher provocation concentration causing a 20% fall in FEV(1), and in those with longer asthma duration. Workers' characteristics, as well as the type of agent they are sensitised to, may help to predict the type of response after specific inhalation challenge.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.042
GPT teacher head0.334
Teacher spread0.293 · 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 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

Citations35
Published2009
Admission routes2
Has abstractyes

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