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Record W2796254030 · doi:10.1155/2018/5076259

Optimizing the Delivery of Inhaled Medication for Respiratory Patients: The Role of Valved Holding Chambers

2018· review· en· W2796254030 on OpenAlexafffund
Andrew McIvor, Hollie Devlin, Alan Kaplan

Bibliographic record

VenueCanadian Respiratory Journal · 2018
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of TorontoMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersGrifolsMylanNovo NordiskSanofiPurdue UniversityTeva Pharmaceutical IndustriesAstraZenecaMcMaster UniversityPfizer
KeywordsMedicineInhalationAnesthesiaAsthmaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Valved holding chambers (VHCs) have been used with pressurized metered-dose inhalers since the early 1980s. They have been shown to increase fine particle delivery to the lungs, decrease oropharyngeal deposition, and reduce side effects such as throat irritation, dysphonia, and oral candidiasis that are common with use of pressurized metered-dose inhalers (pMDIs) alone. VHCs act as aerosol reservoirs, allowing the user to actuate the pMDI device and then inhale the medication in a two-step process that helps users overcome challenges in coordinating pMDI actuation with inhalation. The design of VHC devices can have an impact on performance. Features such as antistatic properties, effective face-to-facemask seal feedback whistles indicating correct inhalation speed, and inhalation indicators all help improve function and performance, and have been demonstrated to improve asthma control, reduce the rate of exacerbations, and improve quality of life. Not all VHCs are the same, and they are not interchangeable. Each pairing of a pMDI device plus VHC should be considered as a unique delivery system.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.306
Teacher spread0.258 · 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 designNot applicable
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

Citations24
Published2018
Admission routes2
Has abstractyes

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