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Record W4414684568 · doi:10.33590/emj/rebz6731

How a Novel Portable Spacer Benefits Patients with Respiratory Disease, the Healthcare System, and the Environment

2025· article· en· W4414684568 on OpenAlexaboutno aff
Jason Suggett, Sheila Wang

Bibliographic record

VenueEuropean Medical Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsInhalerHealth careMetered-dose inhalerDry-powder inhalerGold standard (test)Airway

Abstract

fetched live from OpenAlex

Spacers or valved holding chambers added to pressurised metered dose inhalers (pMDI) reduce errors in inhaler technique and improve delivery of fine dose particles to patients with obstructive airway diseases. However, despite the established benefits of spacers, patients frequently do not use them beyond the home setting due to inconvenience regarding their relatively large size and appearance. Notably, not using the spacer (i.e., nonadherence) could have negative consequences for more than just the patients themselves. Alan Kaplan, Physician and Chairperson of the Family Physician Airways Group of Canada, Markham, Ontario, Canada; and Job F.M. Van Boven, Associate Professor of Cost-Effective & Sustainable Respiratory Drug Use, University Medical Center Groningen, the Netherlands, explored the intricate relationships between adherence to prescribed treatments with spacers, symptom control and management, the carbon footprint, and economic burden on the healthcare system. They also introduced a novel, portable spacer that was designed to be used while on the go. Specifically, they discussed the performance of the portable spacer compared to the gold standard traditional spacer, pMDIs alone, and dry powder inhalers (DPI). Finally, summarising patient feedback and adherence data about the new portable spacer.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.214
Teacher spread0.204 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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