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Record W1582663428 · doi:10.1159/000107054

Modeling Responses to Respiratory House Dust Mite Exposure

2007· review· en· W1582663428 on OpenAlexafffund
Elizabeth C. Cates, Ramzi Fattouh, Jill R. Johnson, Alba Llop‐Guevara, Manel Jordana

Bibliographic record

VenueContributions to microbiology · 2007
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersCanadian Institutes of Health ResearchCanada Research Chairs“la Caixa” Foundation
KeywordsHouse dust miteImmunologyAeroallergenOvalbuminSensitizationAsthmaInflammationAllergic inflammationAllergyImmune systemMedicineAllergen

Abstract

fetched live from OpenAlex

House dust mite (HDM) is the most pervasive indoor aeroallergen source worldwide. Allergens derived from HDM are associated with sensitization and allergic asthma. Allergic asthma is an immunologically driven disease characterized by a Th2-polarized immune response, eosinophilic inflammation, airway hyperreactivity, and remodeling. Animal models of asthma utilizing ovalbumin (OVA) exposure have afforded us considerable insight with respect to the mediators and cell types involved in allergic airway inflammation. However, OVA preparations and HDM are two vastly different materials. This chapter is specifically concerned with modeling responses to HDM exposure in mice. These studies have furnished new information and unlocked new lines of inquiry regarding biological responses to common aeroallergens. The complexity of HDM as an allergen source, with its plethora of protein and nonprotein immunogenic components, may influence the mechanisms underlying sensitization, inflammation and remodeling. Here, we will discuss this issue, along with giving critical thought to the use of experimental models.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.057
GPT teacher head0.382
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations51
Published2007
Admission routes2
Has abstractyes

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