Ventilatory Support of Patients with Sepsis or Septic Shock in Resource-Limited Settings
Bibliographic record
Abstract
In this chapter we discuss recommendations on the identification of patients with acute respiratory distress syndrome (ARDS), indications for mechanical ventilation, and strategies for lung-protective ventilation in resource-limited settings. Where blood gas analyzers are unavailable, it can be replaced by the plethysmographic oxygen saturation/fractional inspirational oxygen concentration (SpO 2/ FiO 2 ) gradient. Bedside lung ultrasound is a valuable diagnostic tool assessing pulmonary edema and other pathologies. A number of recommendations for safe and lung-protective mechanical ventilation in patients with sepsis and respiratory failure are provided. However, many of these have not been trialed specifically in resource-limited settings. These recommendations include an elevated head-of-bed position and a minimum level of positive end-expiratory pressure (PEEP) of 5 cm H 2 O only to be in patients with moderate or severe ARDS. In addition, low FiO 2 and low oxygenation goals are suggested, using PEEP/FiO 2 tables. Recruitment maneuvers are indicated in refractory hypoxia, but require experienced staff. Low tidal volumes (5–7 ml/kg predicted body weight, avoiding >10 ml/kg) are recommended and if at all possible in combination with end-tidal carbon dioxide (CO 2 ) monitoring for recognition of dislodgement of the endotracheal tube and under- or overventilation. “Volume-controlled” modes could be safer than “pressure-controlled” modes, and we recommend to check regularly whether a patient tolerates a “support” mode; we also suggest to perform spontaneous breathing trials to timely identify patients who are ready for extubation, but also to plan extubating patients when sufficient staff is around to guarantee safe re-intubation.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".