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Record W3163464079 · doi:10.1080/24745332.2021.1898845

Mechanical insufflation-exsufflation and available funding for Canadian adult patients. A Canadian Thoracic Society Position Statement

2021· article· en· W3163464079 on OpenAlexaffabout
Karla J. Horvey, Lacey Nairn Pederson, Marco Zaccagnini

Bibliographic record

VenueCanadian Journal of Respiratory Critical Care and Sleep Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversity of SaskatchewanSaskatchewan Health Authority
Fundersnot available
KeywordsExsufflationMedicineInsufflationIntensive care medicineNeuromuscular diseaseHealth careHealth professionalsDiseaseInternal medicineAnesthesiaPolitical science

Abstract

fetched live from OpenAlex

Many neuromuscular disease patient populations suffer from a weak, inadequate cough, which may lead to respiratory tract infections, respiratory failure, and increased mortality. Hospitalized neuromuscular disease patients are often treated with a mechanical insufflation-exsufflation (MI-E) machine to improve lung volume, promote mucociliary clearance and improve their respiratory health. Many of these patients require MI-E within their homes to maintain the benefits achieved in hospitals. Currently, a resource paper that outlines provincial funding avenues for home MI-E machines does not exist. Accordingly, Canadian Respiratory Health Professionals (CRHP) Leadership Council members formed a working group to propose and collate recommendations and resources for using MI-E in neuromuscular populations at home.

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)0.012
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.077
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0060.002
Scholarly communication0.0050.002
Open science0.0040.004
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0270.003

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 head0.041
GPT teacher head0.319
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2021
Admission routes2
Has abstractyes

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