Noninvasive monitoring of CO2 during polysomnography
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sleep-related breathing disorders are complex conditions that require the integration of clinical and sleep laboratory findings to support a diagnosis. Analysis of carbon dioxide (CO2) levels during sleep provides important additional information to the clinician that is not obtained from other polysomnographic indices, and that may have a direct impact on both diagnosis and patient mortality. Although arterial blood gas (ABG) is considered the gold standard for assessing PaCO2 levels, there are numerous drawbacks. Noninvasive methods for PaCO2 estimation include end-tidal and transcutaneous monitoring, which allow for continuous monitoring of trends. RECENT FINDINGS: Review of the recent literature suggests that transcutaneous methods correlate strongly with PaCO2 levels and can provide an accurate surrogate in replacement of ABGs. End-tidal methods provide breath to breath information that can be used to assess hypoventilation; however, they have more variability, especially in patients with increased dead space and small tidal volumes. To date, however, there are limited studies investigating noninvasive CO2 monitoring during sleep. SUMMARY: Given the benefits of CO2 monitoring and the importance of assessing for hypercapnia, noninvasive continuous CO2 monitoring should be considered for all patients undergoing polysomnography.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".