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Adjuvants to Mechanical Ventilation for Acute Respiratory Failure. Adoption, De-adoption, and Factors Associated with Selection

2016· article· en· W2530272623 on OpenAlexaff
Laveena Munshi, Hayley B. Gershengorn, Eddy Fan, Hannah Wunsch, Niall D. Ferguson, Thérèse A. Stukel, Gordon D. Rubenfeld

Bibliographic record

VenueAnnals of the American Thoracic Society · 2016
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity Health NetworkUniversity of TorontoMount Sinai HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMechanical ventilationExtracorporeal membrane oxygenationNitric oxideAdjuvantInhalationAnesthesiaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

RATIONALE: Adoption and de-adoption of adjuvant strategies to mechanical ventilation for acute respiratory failure (ARF), and factors associated with their selection, have not been extensively evaluated. OBJECTIVES: To evaluate change in use of adjuvants to mechanical ventilation for ARF (2008-2013), the impact of landmark publications on adoption and de-adoption, and factors associated with use. METHODS: Changes in use of four adjuvants for ARF from 2008 to 2013, the impact of landmark publications on use, and factors associated with use were evaluated with the Premier Database. Extracorporeal membrane oxygenation (ECMO), inhaled nitric oxide, inhaled epoprostenol, and continuous neuromuscular blockading agents (cNMBAs) in adult mechanically ventilated patients were identified on the basis of International Classification of Diseases, Ninth Revision, Clinical Modification codes and billing data. MEASUREMENTS AND MAIN RESULTS: Among 514,913 patients with ARF, 11,567 (2.3%) were treated with at least one adjuvant. cNMBAs were the most frequently used adjuvants (n = 10,073, 2.1% in capable hospitals), followed by inhaled pulmonary vasodilators (n = 1,878, 1.0% in capable hospitals; 58% nitric oxide), and ECMO (n = 195, 0.2% in capable hospitals). There was an increase in ECMO and inhaled epoprostenol over time but no change in nitric oxide or cNMBAs. Segmented regression analysis was used to evaluate whether clinical practice was in accordance with emerging evidence from landmark studies. Using the relevant landmark publication dates, these analyses did not reveal any change in use over time after publication with the exception of inhaled epoprostenol-for which rates of growth decreased over time, possibly in response to the evidence. There was a significant amount of variability in patient and hospital factors associated with use with between adjuvants. CONCLUSIONS: Between 2008 and 2013, there was an increase in use of ECMO and inhaled epoprostenol, and no change in use of inhaled nitric oxide or continuous intravenous infusion of a neuromuscular blocking agent. There was considerable variability in patient and hospital factors associated with use across different adjuvants.

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)0.015
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.097
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.386
Teacher spread0.293 · 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.

Study designObservational
DomainMethods
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".

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Citations23
Published2016
Admission routes1
Has abstractyes

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