Consensus document on controversial issues for the treatment of hospital-associated pneumonia
Bibliographic record
Abstract
BACKGROUND: Hospital-associated pneumonia (HAP) remains an important cause of morbidity and mortality despite advances in antimicrobial therapy. Many aspects of the treatment of HAP caused by multi-resistant Gram-positive microorganisms have been extensively studied, but controversial issues remain. CONTROVERSIAL ISSUES: The aim of this GISIG (Gruppo Italiano di Studio sulle Infezioni Gravi) working group - a panel of multidisciplinary experts - was to define recommendations for some controversial issues using an evidence-based and analytical approach. The controversial issues were: (1) Is combination antibiotic therapy or monotherapy more effective in the treatment of HAP? (2) What role do pharmacokinetic/pharmacodynamic antibiotic features have as a guide in the selection of treatment for HAP? (3) Is a de-escalation approach for the management of HAP effective? An analysis of the studies published up until April 2009 is presented and discussed in detail. METHODS: A systematic literature search using PubMed, MEDLINE, and EMBASE databases and the Cochrane Library was performed. A matrix was created to extract evidence from original studies using the CONSORT method to evaluate randomized clinical trials and the Newcastle-Ottawa Quality Assessment Scale for case-control studies, longitudinal cohorts, and retrospective studies. The GRADE method for grading quality of evidence was applied.
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 | 0.139 | 0.223 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.027 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".