Oxygen targets in critically ill patients: from pathophysiology to population enrichment strategies
Bibliographic record
Abstract
Oxygen supplementation is widely used to enhance oxygen delivery and to treat or prevent hypoxia; however, it requires careful management to avoid the harmful effects of excessive oxygen exposure. Both hyperoxia (inspiratory oxygen fraction exceeding 0.21) and hyperoxemia (arterial oxygen tension oxygen partial pressure [PaO2] > 100 mmHg) can contribute to lung injury, promote systemic vasoconstriction, and increase the production of reactive oxygen species, which can impair macromolecular and cellular functions. Conversely, in certain situations, hyperoxemia may provide benefits, such as hemodynamic stabilization in hyperdynamic shock, immunomodulation, and bactericidal effects. The literature presents conflicting evidence regarding the impact of different oxygen targets (i.e., PaO2 and/or peripheral saturation of oxygen [SpO2]) on both short- and long-term outcomes in patients with acute critical conditions, such as acute respiratory distress syndrome, sepsis, cardiac arrest, and acute central nervous system injuries. These discrepancies may stem from the small differences between the oxygenation targets used in randomized trials, the physiological limitations of PaO2 and SpO2 targets, which reflect blood oxygen content rather than oxygen delivery, the lack of measurements of microvascular function or oxygen delivery, and the heterogeneity in treatment response. Furthermore, advanced analytical methods (e.g., machine learning) are emerging as promising tools to implement population enrichment strategies. By refining patient sub-group identification, these approaches can significantly optimize precision medicine, enabling more personalized oxygen therapy tailored to individual patient characteristics.
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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 | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".