Modified oxygen mask to induce target levels of hyperoxia and hypercarbia during radiotherapy: A more effective alternative to carbogen
Bibliographic record
Abstract
PURPOSE: Carbogen has long been under investigation as an adjuvant to radiotherapy of tumors. A major factor confounding its evaluation is its inconsistency in raising blood partial pressure of CO(2) (pCO(2)). We investigated whether a new partial rebreathing method would provide better control of pCO(2) than carbogen. METHODS AND MATERIALS: We compared the efficacy of each method in 10 healthy volunteers. Volunteers breathed 1.5, 3 and 5% carbogen in 5-min stages via the usual non-rebreathing circuit. All the volunteers then breathed 100% O(2) through a commercial sequential gas delivery (SGD) circuit modified by attaching a reservoir to its exhalation port. Hypercarbia was induced by step reductions in oxygen flow to the SGD circuit. We monitored minute ventilation and end-tidal pCO(2) (ETpCO(2)) as a surrogate for its arterial value. RESULTS: Inhalation of 1.5 and 3% carbogen did not increase ETpCO(2) from baseline (40 +/- 1.5 mmHg); 5% carbogen increased ETpCO(2) to 45 +/- 1.6 mmHg (p < 0.001). With the SGD circuit, reducing O(2) flow to 4.3 +/- 0.7 l/min increased ETpCO(2) in all subjects from 41 +/- 2.0 mmHg (baseline) to 46 +/- 2.1 mmHg (p < 0.001). Voluntary hyperventilation reduced ETpCO(2) with 5% carbogen but not with SGD (p = 0.379). CONCLUSIONS: We confirm previous observations that carbogen inhalation does not result in a predictable rise in ETpCO(2) and suggest that a precise and stable target ETpCO(2) can instead be induced by simply controlling O(2) flow into a modified SGD circuit. We hoped that the reliable control of pCO(2) will enable studies that address first, the efficacy of raising ETpCO(2) on specific tumor blood flow, and eventually, its benefit as an adjuvant to radiotherapy.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".