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Care Practices and Health-related Quality of Life for Individuals Receiving Assisted Ventilation. A Cross-National Study

2016· article· en· W2416495435 on OpenAlexaffabout
Liam M. Hannan, Hamna Sahi, Jeremy Road, Christine F. McDonald, David J. Berlowitz, Mark E. Howard

Bibliographic record

VenueAnnals of the American Thoracic Society · 2016
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMechanical ventilationPopulationQuality of life (healthcare)Obesity hypoventilation syndromeVentilation (architecture)HypoventilationHealth careEmergency medicineNational Health Interview SurveySocioeconomic statusIntensive care medicineGerontologyPhysical therapyEnvironmental healthObesityNursingRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

RATIONALE: Comparisons of home mechanical ventilation services have demonstrated considerable regional variation in patient populations managed with this therapy. The respiratory care practices used to support individuals receiving assisted ventilation also appear to vary, but they are not well described. It is uncertain whether differences in the approach to care could influence health outcomes for individuals receiving assisted ventilation. OBJECTIVES: We sought to identify and describe the respiratory care practices of home ventilation providers in two different regions and determine whether care practice differences influence health-related quality of life. METHODS: We conducted a cross-national survey of individuals receiving assisted ventilation managed by two statewide home mechanical ventilation providers, one in Victoria, Australia, and the other in British Columbia, Canada. The survey was used to evaluate care practices, functional and physical measures, socioeconomic attributes, and health-related quality of life. MEASUREMENTS AND MAIN RESULTS: Overall, 495 individuals receiving assisted ventilation (57.2%) responded to the survey. Responders had clinical attributes similar to those of nonresponders. The Canadian population had a greater proportion of individuals with neuromuscular disorders and lesser percentages with obesity hypoventilation syndrome and chronic obstructive pulmonary disease. We also found marked differences in the reported care practices in Canada that were not fully explained by population differences. Subjects in the Canadian sample were more likely than their Australian counterparts to use invasive mechanical ventilation (24.2% vs. 2.5%; P < 0.001), to use routine airway clearance techniques (28.9% vs. 14.8%; P < 0.001), and to have had home implementation of noninvasive ventilation (39.9% vs. 3.6%; P < 0.001). Subjects in the Australian population were more likely than those in Canada to have undergone polysomnography to evaluate their ventilatory support (93.9% vs. 37.4%; P < 0.001). There was no difference in summary measures of health-related quality of life between the two sites. In a multivariable regression model, age, ability to perform activities of daily living, physical function, employment, and household income were all independently associated with health-related quality of life, but neither geographic location (Canada vs. Australia) nor underlying diagnosis were significant factors in the model. CONCLUSIONS: In two cohorts of individuals receiving assisted ventilation, one in Australia and the other in Canada, we found marked differences in both the care practices employed and the populations served. Despite these regional differences, measures of health-related quality of life were not different. Further research is required to examine costly or burdensome interventions that are currently used routinely in the management of individuals receiving assisted ventilation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.345
GPT teacher head0.532
Teacher spread0.188 · 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 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

Citations20
Published2016
Admission routes2
Has abstractyes

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