Neonatal total liquid ventilation: is low-frequency forced oscillation technique suitable for respiratory mechanics assessment?
Bibliographic record
Abstract
This study aimed to implement low-frequency forced oscillation technique (LFFOT) in neonatal total liquid ventilation (TLV) and to provide the first insight into respiratory impedance under this new modality of ventilation. Thirteen newborn lambs, weighing 2.5 + or - 0.4 kg (mean + or - SD), were premedicated, intubated, anesthetized, and then placed under TLV using a specially design liquid ventilator and a perfluorocarbon. The respiratory mechanics measurements protocol was started immediately after TLV initiation. Three blocks of measurements were first performed: one during initial respiratory system adaptation to TLV, followed by two other series during steady-state conditions. Lambs were then divided into two groups before undergoing another three blocks of measurements: the first group received a 10-min intravenous infusion of salbutamol (1.5 microg x kg(-1) x min(-1)) after continuous infusion of methacholine (9 microg x kg(-1) x min(-1)), while the second group of lambs was chest strapped. Respiratory impedance was measured using serial single-frequency tests at frequencies ranging between 0.05 and 2 Hz and then fitted with a constant-phase model. Harmonic test signals of 0.2 Hz were also launched every 10 min throughout the measurement protocol. Airway resistance and inertance were starkly increased in TLV compared with gas ventilation, with a resonant frequency < or = 1.2 Hz. Resistance of 0.2 Hz and reactance were sensitive to bronchoconstriction and dilation, as well as during compliance reduction. We report successful implementation of LFFOT to neonatal TLV and present the first insight into respiratory impedance under this new modality of ventilation. We show that LFFOT is an effective tool to track respiratory mechanics under TLV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".