Emergence of antimicrobial resistance among Acinetobacter species: a global threat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bacteria within the genus Acinetobacter [principally Acinetobacter baumannii-calcoaceticus complex (ABC)] are Gram-negative coccobacilli that may cause serious nosocomial infections (particularly ventilator-associated pneumonia and infections of the bloodstream, urinary tract, and wounds) as well as community-acquired infections (often skin/soft tissue infections in the context of trauma). Within the past two decades, Acinetobacter spp. have been responsible for an increasing number of infections in intensive care units (ICUs) globally. Treatment of Acinetobacter infections is difficult, as Acinetobacter spp. are intrinsically resistant to multiple antimicrobial agents, and have a remarkable ability to acquire new resistance determinants via multiple mechanisms. RECENT FINDINGS: Since the 1990s, global resistance to antimicrobials has escalated dramatically among ABC. Global spread of multidrug-resistant (MDR) A. baumannii strains reflects dissemination of a few clones between hospitals, geographic regions, and continents; this spread is amplified by excessive use of antibiotics. Many isolates are resistant to all antimicrobials except colistin (polymyxin E), and some infections are untreatable with existing antimicrobial agents. SUMMARY: Antimicrobial resistance poses a serious threat to control infections due to ABC. Strategies to curtail environmental colonization with MDR-ABD will require aggressive infection control efforts and cohorting of infected patients. Thoughtful antibiotic strategies are essential to limit the consequences and spread of MDR-ABC. Optimal therapy will likely require combination antimicrobial therapy of existing antibiotics as well as development of novel antibiotic classes.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it