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Cost Effectiveness Comparison of a Dry Powder Inhaler to a Metered Dose Inhaler plus Valved Holding Chamber based on an In-Vitro Drug Delivery Model

2020· article· en· W3096123597 on OpenAlexaff
Mark Nagel, Jason Suggett, Alison Ellery

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsTrudell Medical International (Canada)
Fundersnot available
KeywordsDry-powder inhalerMedicineInhalerMetered-dose inhalerBudesonideBudesonide/formoterolFormoterolAsthmaDrug deliveryInternal medicineNanotechnology

Abstract

fetched live from OpenAlex

Background: In a previous study, healthy participants were asked to inhale from either a dry powder inhaler (DPI) or metered dose inhaler (MDI) + valved holding chamber (VHC). Results indicated that differing amounts of medication could be available to the lungs, depending on the delivery system used (submitted and accepted ATS 2020). Objective: To compare the relative cost of using a DPI and an MDI+VHC to deliver budesonide/formoterol (BUD/F; Symbicort®) in patients for whom low-dose ICS maintenance therapy is appropriate Methods: Comparisons were based on BUD/F 200/6µg twice daily with yearly cost inputs of £336 for the DPI and £336+5.2 for the MDI + VHC obtained from a relevant source in the United Kingdom (www.rightbreathe.com). Drug delivery data was taken from the aforementioned lab study. Results: The mass (µg) of FF and BUD delivered per £ are reported in the table. Conclusion: From a UK perspective, BUD/F delivered via an MDI + VHC offers an economic benefit for the treatment of patients with mild asthma. Further analysis is required to determine if there would be any quality-adjusted life-year (QALY) effects based on the chosen intervention.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.089
GPT teacher head0.345
Teacher spread0.256 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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