Mechanical ventilation: epidemiological insights into current practices
Bibliographic record
Abstract
PURPOSE OF REVIEW: To describe the trends in the results of epidemiological studies of mechanical ventilation. RECENT FINDINGS: Changes in population demographics have increased the incidence of mechanical ventilation. Higher age and comorbidity rates portend poorer outcomes of mechanical ventilation. The most common indication for initiation of mechanical ventilation is acute respiratory failure, including postoperative respiratory failure, pneumonia, sepsis, and acute respiratory distress syndrome. Patients with sepsis and acute respiratory distress syndrome have a much higher mortality risk than the rest of this population. Changes over time in the selection of modes of ventilation, tidal volumes, positive end-expiratory pressure levels, weaning strategies, and tracheostomy timing appear to accord with data from randomized controlled trials in the literature. However, despite these changes, observational studies have not detected a statistically significant change in adjusted mortality over time. SUMMARY: The burden of critical illness will likely continue to increase in the future. Evidence from randomized trials appears to have affected the management of mechanical ventilation, but adherence to evidence-based practices may not be ideal.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".