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Mechanical Determinants of Airways Hyperresponsiveness

2011· review· en· W2029936365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2011
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAirway hyperresponsivenessAirwayAsthmaBronchial hyperresponsivenessMedicineImmunologyLungNeuroscienceRespiratory diseaseInternal medicineBiologyAnesthesia

Abstract

fetched live from OpenAlex

Asthmatic individuals typically experience exaggerated decrements in their ability to breathe after receiving standardized doses of smooth muscle agonist, a phenomenon known as airways hyperresponsiveness (AHR). Breathing difficulties are caused by excessive narrowing of the pulmonary airways, which is instigated by shortening of the airway smooth muscle (ASM). Exactly why many asthmatic individuals are hyperresponsive, however, remains controversial because of the many varied mechanisms that could possibly be involved. Nevertheless, much of the understanding of AHR comes down to a matter of considering the spatial configuration of the components that make up the airway, and the static and dynamic physical forces these components experience. In this review, we consider these mechanical factors, which are conveniently subdivided into three groups involving (i) the active forces construing to narrow the airways, (ii) the mechanical loads against which these forces must work, and (iii) the geometric transformation of a given degree of ASM shortening into airway narrowing. Each of these groups of factors has potent potential to influence AHR. It is likely, however, that they operate together to produce the AHR characteristic of severe asthma.

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.

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)0.007
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 score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.093
GPT teacher head0.386
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