Update in Mechanical Ventilation, Sedation, and Outcomes 2014
Bibliographic record
Abstract
Novel approaches to the management of acute respiratory distress syndrome include strategies to enhance alveolar liquid clearance, promote epithelial cell growth and recovery after acute lung injury, and individualize ventilator care on the basis of physiological responses. The use of extracorporeal membrane oxygenation (ECMO) is growing rapidly, and centers providing ECMO must strive to meet stringent quality standards such as those set out by the ECMONet working group. Prognostic tools such as the RESP score can assist clinicians in predicting outcomes for patients with severe acute respiratory failure but do not predict whether ECMO will enhance survival. Evidence continues to grow that novel modes of mechanical ventilation such as neurally adjusted ventilatory assist are feasible and improve patient physiology and patient-ventilator interaction; data on clinical outcomes are limited but supportive. Critical illness causes long-term psychological and function sequelae: the risk of a new psychiatric diagnosis and severe physical impairment is significantly increased in the months after discharge from the intensive care unit. These long-term effects might be amenable to changes in sedation practice and increased early mobilization. Daily sedation discontinuation enhances the validity of routine delirium assessment. Many critically ill patients merit assessment by palliative care clinicians; the demand for palliative care services among critically ill patients is expected to grow. Future trials to test therapies for critical illness must ensure that study designs are adequately powered to detect benefit using realistic event rates. Integrating "big data" approaches into treatment decisions and trial designs offers a potential means of individualizing care to enhance outcomes for critically ill patients.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 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".