Clinical Practice Guidelines for Hospital‐Acquired Pneumonia and Ventilator‐Associated Pneumonia in Adults
Bibliographic record
Abstract
Hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) are important causes of morbidity and mortality, with mortality rates approaching 62%. HAP and VAP are the second most common cause of nosocomial infection overall, but are the most common cause documented in the intensive care unit setting. In addition, HAP and VAP produce the highest mortality associated with nosocomial infection. As a result, evidence-based guidelines were prepared detailing the epidemiology, microbial etiology, risk factors and clinical manifestations of HAP and VAP. Furthermore, an approach based on the available data, expert opinion and current practice for the provision of care within the Canadian health care system was used to determine risk stratification schemas to enable appropriate diagnosis, antimicrobial management and nonantimicrobial management of HAP and VAP. Finally, prevention and risk-reduction strategies to reduce the risk of acquiring these infections were collated. Future initiatives to enhance more rapid diagnosis and to effect better treatment for resistant pathogens are necessary to reduce morbidity and improve survival.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".