Oximetry neither to prescribe long-term oxygen therapy nor to screen for severe hypoxaemia
Bibliographic record
Abstract
Background and objective Transcutaneous pulse oximetry saturation (SpO2) is widely used to diagnose severe hypoxaemia and to prescribe long-term oxygen therapy (LTOT) in COPD. This practice is not based on evidence. The primary objective of this study was to determine the accuracy (false positive and false negative rates) of oximetry for prescribing LTOT or for screening for severe hypoxaemia in patients with COPD. Methods In a cross-sectional study, we correlated arterial oxygen saturation (SaO2) andSpO2in patients with COPD and moderate hypoxaemia (n=240) and calculated the false positive and false negative rates ofSaO2at the threshold of ≤88% to identify severe hypoxaemia (arterial oxygen tension (PaO2) ≤55 mmHg orPaO2<60 mmHg) in 452 patients with COPD with moderate or severe hypoxaemia. Results The correlation betweenSaO2andSpO2was only moderate (intra-class coefficient of correlation: 0.43; 95% confidence interval: 0.32–0.53). LTOT would be denied in 40% of truly hypoxaemic patients on the basis of aSaO2>88% (i.e.,false negative result). Conversely, LTOT would be prescribed on the basis of aSaO2≤88% in 2% of patients who would not qualify for LTOT (i.e.,false positive result). Using a screening threshold of ≤92%, 5% of severely hypoxaemic patients would not be referred for further evaluation. Conclusions Several patients who qualify for LTOT would be denied treatment using a prescription threshold of saturation ≤88% or a screening threshold of ≤92%. Prescription of LTOT should be based onPaO2measurement.
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 | 0.008 | 0.051 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".