Pressure support, patient effort and tidal volume: a conceptual model for a non linear interaction
Bibliographic record
Abstract
Pressure support ventilation (PSV) is a form of assisted ventilation which has become frequently used, with the aim of partially unloading the patient’s inspiratory muscles. Both under- and over-assistance should be avoided to target a lung- and diaphragm- protective ventilation. Herein, we propose a conceptual model, supported by actual data, to describe how patient and ventilator share the generation of tidal volume (Vt) in PSV and how respiratory system compliance (Crs) affects this interaction. We describe the presence of a patient-specific range of PSV levels, within which the inspiratory effort (Pmus) is modulated, keeping Vt relatively steady on a desired value (Vt target ). This range of assistance may be considered the “adequate PSV assistance” required by the patient, while higher and lower levels may result in over- and under-assistance respectively. As we also show, the determinants of over- and under- assistance borders depend on the combination of Crs and the inspiratory effort which the patient is able to sustain over a period of time. These concepts can be applied at the bedside to understand if the level of assistance is adequate to patient’s demand, focusing on the variation of relevant parameters (Vt, Pmus and pressure-muscle-index) as patient reaction to a change in the level of assistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".