Consensus document on controversial issues for the treatment of infections of the central nervous system: bacterial brain abscesses
Bibliographic record
Abstract
BACKGROUND: Bacterial brain abscesses remain a serious central nervous system problem despite advances in neurosurgical, neuroimaging, and microbiological techniques and the availability of new antibiotics. The successful treatment of brain abscesses requires surgery, appropriate antibiotic therapy, and eradication of the primary source; nevertheless many controversial issues on the management of this serious infection remain unresolved. 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) Which patients with bacterial brain abscesses can be managed safely using medical treatment alone? (1a) What is the efficacy in terms of outcome, tolerability, cost/efficacy, and quality of life of the different antibiotic regimens used to treat bacterial cerebral abscesses? (1b) Which antibiotics have the best pharmacokinetics and/or tissue penetration of brain and/or brain abscess? 2) What is the best surgical approach in terms of outcome in managing bacterial brain abscesses? Results are presented and discussed in detail. METHODS: A systematic literature search using the MEDLINE database for the period 1988 to 2008 of randomized controlled trials and/or non-randomized studies 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 and strength of recommendation 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".