An audit of hypoxaemia, hyperoxaemia, hypercapnia and acidosis in blood gas specimens: Table 1–
Bibliographic record
Abstract
To the Editors: The emergency management of hypoxaemic patients requires clinicians to avoid the hazard of dangerous hypoxaemia due to under-treatment with oxygen, whilst also avoiding the hazards of hypercapnic respiratory failure (iatrogenic hypercapnia) and oxygen toxicity, which may be caused by over-treatment with oxygen. In the past, many clinicians acted cautiously by giving high concentrations of oxygen to all potentially hypoxaemic patients and a culture evolved that “more is better”. However, since the 1960s, it has been known that some patient groups, particularly those with chronic obstructive pulmonary disease (COPD), are especially vulnerable to uncontrolled oxygen therapy and a recent randomised study showed that mortality in this patient group was doubled when high-concentration oxygen was used compared with controlled oxygen therapy [1–4]. It has also been demonstrated that hyperoxaemia is associated with increased mortality in patients with stroke, and in survivors of cardiac resuscitation and critically ill patients in the intensive care unit (ICU) [5–7]. The British Thoracic Society (BTS) guidelines for emergency oxygen use recommend a target oxygen saturation range of 94–98% for most emergency medical patients and a lower target range of 88–92% for those at risk of hypercapnic respiratory failure …
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".