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Record W7135058069 · doi:10.4187/respcare.20233922955

British Columbia Ventilator Selection Survey

2023· article· en· W7135058069 on OpenAlexaffabout
Bliss Hampel, Jenna Mulholland, Michael MacAulay, Jason Danbrook

Bibliographic record

VenueRespiratory Care · 2023
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsInterior Health
Fundersnot available
KeywordsPurchasingCapital equipmentQuality (philosophy)Capital expenditureNoninvasive ventilationMedical equipmentSelection (genetic algorithm)Nursing staff

Abstract

fetched live from OpenAlex

Background: Purchasing capital equipment such as ventilators is an important decision for providing critical care, as ventilators come with not only significant initial costs, but continuing costs as well. As a result, we sought out more information about what factors are considered when purchasing a ventilator beyond the newest high-tech mode or feature. Methods: A 29-question survey was distributed amongst RT department leaders in British Columbia, Canada, 21 leaders responded. Hospitals ranged in bed size: 14% ≤ 55, 19% 56-200, 48% 201-400, and 19% ≥ 400 funded beds. The ages of patients ventilated on a daily basis were: 14% birth-18 years, 38% birth-30 days AND ≥ 18 years old, and 48% ≥ 18 years old. Results: Feedback on ventilators from different professions was assessed with ‘very important’ being selected 91% of the time for the site’s own RTs, other hospital RTs 48%, biomed 19%, nursing 5%, medical directors 0%; feedback was considered ‘not important at all’ or ‘slightly important’ 0% of the time for the site’s own RTs, other hospital RTs 0%, biomed 14%, nursing 76%, and medical directors 62%. When assessing ventilator modes, 86% felt high quality noninvasive ventilation (NIV) was ‘fairly important’ or ‘very important’ and 81% felt neonatal invasive and NIV was ‘important or very important’. 62% felt high quality HFNC was ‘fairly important’ or ‘very important’. Capital funds are becoming tighter although 33% felt the purchase price was ‘not important at all’ or ‘slightly important’; while purchase price was ‘fairly important’ or ‘very important’ 29% of time. Ongoing costs were ‘fairly important’ or ‘very important’ for preventative maintenance 19%, proprietary supplies 29%, and warranty 14%. In regard to vendors, 67% of leaders reported both trust with the sales representative and the vendor’s ability to provide quality, in person, hands-on education as ‘very important’ to them. 48% said vendor technical knowledge is ‘very important’, and 52% reported history of support and response time as ‘very important’. Conclusions: This study examined interdisciplinary feedback, ventilator capabilities, price and quality, plus relationships with vendors and how each plays a role in the decision-making process. Although financial pressures continue to increase, this study revealed that quality care is still the most important goal highlighted by the importance of frontline RT feedback and the ventilator's ability to provide high quality neonatal modes, NIV, and HFNC.Importance of various modes/adjuncts once the decision is made to purchase a ventilator(s)Mode/AdjunctVery ImportantFairly ImportantImportantSlightly ImporantNot Important at AllAutomatically Wean "Simple to Wean" patients9%0%14%48%29%Automatically Wean "Difficult to Wean" patients0%0%38%24%38%High Quality Heated Humidified High Flow Oxygen19%43%19%9%10%High Quality Non-Invasive Ventilation57%29%14%0%0%Quality Invasive & Non-Invasive Ventilation for Neonates/Pediatrics43%38%10%0%9%Importance of costs associated with purchasing a new ventilator(s)CostsVery ImportantFairly ImportantImportantSlightly ImportantNot Important at AllPurchase Price of Ventilator5%24%38%28%5%Warranty5%9%48%24%14%Preventative Maintenance (Biomed Time/Supplies)14%5%38%29%14%Proprietary Ventilator Supplies10%19%52%19%0%Training Staff14%10%14%43%19%

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.069
GPT teacher head0.376
Teacher spread0.307 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes2
Has abstractyes

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