Respiratory Monitoring Practices During Procedural Sedation and Analgesia in the Cardiac Catheterisation Lab Could Be Enhanced by Using Capnography to Assess Ventilation
Bibliographic record
Abstract
We report on the risk profile of the first 262 (target of 320 now recruited) patients. Results: At discharge, mean age was 71± 11 years and 53% are male, with 68% and 32% being initially managed as “rate” versus “rhythm” control (mean heart rate 77± 16 versus 72± 16 beats/minute). Common comorbidity includes hypertension (73%), coronary artery disease (34%), type 2 diabetes (32%) and stroke (13%). On echocardiography, 89% had left atrial enlargement. Median CHA2DS2-VASc score at baseline was 3 (IQR 0–8).Montreal Cognitive Assessment scores (mean 23± 4) at baseline showed 70% had mild cognitive impairment likely to impair the ability to self-care. Continuous 24 hour ECG Holter monitoring at the intervention home visit revealed that only 50 (47%) had controlled heart rate while 31% and 22%, respectively, had uncontrolled or labile rate or rhythm control. Clinically significant arrhythmias were identified in 49% of intervention patients. Conclusions: The conundrum of maximising the benefit of potentially harmful therapeutics in high-risk AF patients is obvious. The potential for the SAFETY intervention to achieve a balanced approach to management and, therefore, reduce morbidity and mortality relative to usual post-discharge care is also obvious. http://dx.doi.org/10.1016/j.hlc.2012.05.702
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".