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Record W2312498973 · doi:10.1097/ccm.0b013e3182186d09

Quantitative computed tomography in porcine lung injury with variable versus conventional ventilation: Recruitment and surfactant replacement*

2011· article· en· W2312498973 on OpenAlexaff
M. Ruth Graham, Andrew L. Goertzen, Linda Girling, Talia Friedman, Ryan J. Pauls, Timothy J. Dickson, Ainsley E. G. Espenell, W. Alan C. Mutch

Bibliographic record

VenueCritical Care Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of ManitobaResearch Manitoba
Fundersnot available
KeywordsVentilation (architecture)Mechanical ventilationTidal volumeMedicinePulmonary surfactantAnesthesiaRespiratory distressRespiratory systemChemistryInternal medicineBiochemistryPhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: Biologically variable ventilation improves lung function in acute respiratory distress models. If enhanced recruitment is responsible for these results, then biologically variable ventilation might promote distribution of exogenous surfactant to nonaerated areas. Our objectives were to confirm model predictions of enhanced recruitment with biologically variable ventilation using computed tomography and to determine whether surfactant replacement with biologically variable ventilation provides additional benefit in a porcine oleic acid injury model. DESIGN: Prospective, randomized, controlled experimental animal investigation. SETTING: University research laboratory. SUBJECTS: Domestic pigs. INTERVENTIONS: Standardized oleic acid lung injury in pigs randomized to conventional mechanical ventilation or biologically variable ventilation with or without green dye labeled surfactant replacement. MEASUREMENTS AND MAIN RESULTS: Computed tomography-derived total and regional masses and volumes were determined at injury and after 4 hrs of ventilation at the same average low tidal volume and minute ventilation. Hemodynamics, gas exchange, and lung mechanics were determined hourly. Surfactant distribution was determined in postmortem cut lung sections. Biologically variable ventilation alone resulted in 7% recruitment of nonaerated regions (p < .03) and 15% recruitment of nonaerated and poorly aerated regions combined (p < .04). Total and normally aerated regional volumes increased significantly with biologically variable ventilation, biologically variable ventilation with surfactant replacement, and conventional mechanical ventilation with surfactant replacement, while poorly and nonaerated regions decreased after 4 hrs of ventilation with biologically variable ventilation alone (p < .01). Biologically variable ventilation showed the greatest improvement (p < .003, biologically variable ventilation vs. all other groups). Hyperaerated regional gas volume increased significantly with biologically variable ventilation, biologically variable ventilation with surfactant replacement, and conventional mechanical ventilation with surfactant replacement. Biologically variable ventilation was associated with restoration of respiratory compliance to preinjury levels and significantly greater improvements in gas exchange at lower peak airway pressures compared to all other groups. Paradoxically, gas exchange and lung mechanics were impaired to a greater extent initially with biologically variable ventilation with surfactant replacement. Peak airway pressure was greater in surfactant-treated animals with either ventilation mode. Surfactant was distributed to the more caudal/injured lung sections with biologically variable ventilation. CONCLUSIONS: Quantitative computed tomography analysis confirms lung recruitment with biologically variable ventilation in a porcine oleic acid injury model. Surfactant replacement with biologically variable ventilation provided no additional recruitment benefit and may in fact be harmful.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.361
Teacher spread0.261 · 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 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

Citations41
Published2011
Admission routes1
Has abstractyes

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