Bibliographic record
Abstract
: Obstructive sleep apnoea (OSA) is a common breathing disorder of sleep with a prevalence increasing in parallel with the worldwide rise in obesity. Alterations in sleep duration and architecture, hypersomnolence, abnormal gas exchange and also associated comorbidities may all feature in affected patients.The peri-operative period poses a special challenge for surgical patients with OSA who are often undiagnosed, and are at an increased risk for complications including pulmonary and cardiovascular, during that time. In order to ensure the best peri-operative management, anaesthetists caring for these patients should have a thorough understanding of the disorder, and be aware of the individual's peri-operative risk constellation, which depends on the severity and phenotype of OSA, the invasiveness of the surgical procedure, anaesthesia and also the requirement for postoperative opioids.The objective of this review is to educate clinicians in the epidemiology, pathogenesis and diagnosis of OSA in adults and also to highlight specific tasks in the preoperative assessment, namely to select a suitable intra-operative anaesthesia regimen, and manage the extent and duration of postoperative care to facilitate the best peri-operative outcome.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".