Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Molecular hydrogen (H 2 ) is a biologically active gas that is widely used in the healthcare sector. In recent years, on-site H 2 gas generators, which produce high-purity H 2 by water electrolysis, have begun to be introduced in hospitals, clinics, beauty salons, and fitness clubs because of their ease of use. In general, these generators produce H 2 at a low-flow rate, so physicians are concerned that an effective blood concentration of H 2 may not be ensured when the gas is delivered through a nasal cannula. Therefore, this study aimed to evaluate blood concentrations of H 2 delivered from an H 2 gas generator via a nasal cannula. Methods: We administered 100% H 2 , produced by an H 2 gas generator, at a low-flow rate of 250 mL/min via a nasal cannula to three spontaneously breathing micro miniature pigs. An oxygen mask was placed over the nasal cannula to administer oxygen while minimizing H 2 leakage, and a catheter was inserted into the carotid artery to monitor the arterial blood H 2 concentration. Results: During the first hour of H 2 inhalation, the mean (standard error (SE)) H 2 concentrations and saturations in the arterial blood of the three pigs were 1,560 (413) nL/mL and 8.85% (2.34%); 1,190 (102) nL/mL and 6.74% (0.58%); and 1,740 (181) nL/mL and 9.88% (1.03%), respectively. These values are comparable to the concentration one would expect if 100% of the H 2 released from the H 2 gas generator is taken up by the body. Conclusions: Inhalation of 100% H 2 produced by an H 2 gas generator, even at low-flow rates, can increase blood H 2 concentrations to levels that previous non-clinical and clinical studies demonstrated to be therapeutically effective. The combination of a nasal cannula and an oxygen mask is a convenient way to reduce H 2 leakage while maintaining oxygenation. J Clin Med Res. 2020;12(10):674-680 doi: https://doi.org/10.14740/jocmr4323
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it