Go with the flow—clinical importance of flow curves during mechanical ventilation: A narrative review
Bibliographic record
Abstract
Most clinicians pay attention to tidal volume and airway pressures and their curves during mechanical ventilation. On the other hand, inspiratory-expiratory flow curves also provide a plethora of information, but much less attention is paid to them. Flow curves chronologically show the velocity and direction of inspiration and expiration and are influenced by the respiratory mechanics, the patient's effort, and the mode of ventilation and its settings. When the ventilator setting does not synchronize with the patient's respiratory pattern, the patient can easily have worsening breathing effort, patient-ventilator asynchrony, which can lead to prolonged ventilator support or lung injury. The information provided by the flow curves during mechanical ventilation, such as respiratory mechanics, the patient's effort, and patient-ventilator interactions, are very helpful when adjusting the ventilator setting. If clinicians can monitor and assess the flow curves information appropriately, it can be a useful diagnostic and therapeutic tool at the bedside. There may be association between inspiratory effort and flow, and this may further guide us, especially in the weaning process and when patients are not synchronizing with the ventilator. In this review, we try to gather information about "flow" that is scattered around in the literature and textbooks in one place. We will summarize the different flow waveforms utilized in commonly used ventilator modes with their advantages and disadvantages, information gained by the flow curves (i.e., flow-time, flow-volume, and flow-pressure), how to detect and manage asynchronies, and some ideas for future uses. Flow waveforms shapes and patterns are very beneficial for the management of patients undergoing mechanical ventilatory support. Attention to those waveforms can potentially improve patient outcomes. Clinicians should be familiar with this information and how to act upon them.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".