Potential Studies of Mode of Ventilation in Inhalation Injury
Bibliographic record
Abstract
Future studies of modes of ventilation after inhalation injury fall into two categories: 1) optimizing a specific ventilator mode's use in inhalation injury followed by 2) comparison of the different ventilator modes in inhalation injury. The key to determining optimal ventilator strategies is the use of well-defined hypotheses in conjunction with meticulous study design. Studies assessing modes of ventilation after smoke inhalation injury should include attention to several issues that arose during the trials of low tidal volume therapy. Ventilatory goals need to be clarified, and protocols for ventilator adjustments need to be developed. For example, during permissive hypercapnia, how low can the pH descend without needing treatment, and when respiratory acidosis does need treatment, should it be done with sodium bicarbonate, increased ventilation rate, or larger tidal volumes need to be clarified prior to study initiation. When tidal volumes are calculated should they be reported as related to predicted or measured body weight? Should the interventions be based on tidal volume or plateau pressure? What levels of positive end expiratory pressure should be used? Should chest wall compliance and intraabdominal pressures be measured? Dependent variables should be expanded beyond mortality to include:
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.
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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".