Past, present and future of conservative oxygen therapy in critical care
Bibliographic record
Abstract
Conservative oxygen therapy (COT) is the administration lower levels of supplemental\noxygen than usual in order to tolerate a lower level of arterial oxygenation (either the partial\npressure (PaO2) or haemoglobin saturation (SaO2)) than normal. Its purpose is to reduce a\npatient’s overall exposure to additional oxygen in order to minimise the risk of oxyen\ntoxicity.1 This approach to oxygen therapy has also been called permissive hypoxaemia\n(PH) and the terms are frequently used interchangeably; here, we refer to all efforts to\nreduce supplemental oxygen administration or arterial oxygenation as COT. Studies have\nbeen conducted across a wide range of medical conditions, to determine whether COT\nimproves patient outcomes and there appears to be a signal of benefit among acutely unwell\npatients.2 The intention in this article, however, is to focus only on critically ill patients\nadmitted to intensive care units (ICUs). These patients often present with acute hypoxaemic\nrespiratory failure and require high concentration oxygen to restore normal arterial\noxygenation. There is concern thatone of the central pillars of support for these patients,\noxygen, may inadvertently be causing them harm, which we mistakenly ascribe to a\nworsening of their underlying pathology. There remains no consensus on how or when to\nuse COT in critically ill patients and it is imperative we address these questions as soon as\npossible.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".