Reply to Wang <i>et al.</i>
Bibliographic record
Abstract
Variation)/DP L (transpulmonary pressure) for calculating pixel compliance in patients with spontaneous breathing, regardless of the type of tidal impedance (global or the sum of all pixel TIV calculations) used.Finally, for a given stress, the lung strain may be quite different, depending on the size of the lung.At the same time, the strain on the lungs is changing because of changes in endexpiratory lung capacity during the treatment with helmet CPAP or helmet NIV, and the opening of the dorsal alveoli.The lungs exhibit viscoelastic behavior, whereby their stress response is dependent on both the amplitude and rate of strain (4).Therefore, it is debatable to generalize the end-expiratory lung impedance (EELI) derived from k = 13.7.In addition, it should be noted that the ratio between the two derivatives cannot be assumed to be identical to that of their absolute values, as shown in Equation 2b: EELI pixel;abs EELI lung;abs 5 EELI pixel;derived EELI lung;derived : The calculation of EELI depends on the selected reference point for image reconstruction, and the absolute value is not unique.The authors correctly pointed out this issue in chapter 5 of the supplement, so we are unable to follow why they would still calculate the EELI lung using Equation 1b.This is a fascinating study, as both EIT and esophageal pressure are potent tools.The combination of these two instruments yields additional indicators that warrant further exploration.The critical issues in the EIT data analysis posed questions on the present findings, which require further clarification.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.025 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".