Bibliographic record
Abstract
Serratia marcescens is an opportunistic nosocomial pathogen with significant implications for burn care due to its multidrug resistance, virulence, and ability to colonize hospital environments. This retrospective study, conducted at an American Burn Association Verified Burn Centre, reviewed 22 cases of S. marcescens infections from 2015 to 2020. Patients exhibited a mean total body surface area (TBSA) burned of 28% (range: 2%-71%), with 68% sustaining burns >20% TBSA and 40.9% presenting with inhalation injuries. The pathogen was most commonly isolated from sputum (36%) and burn wound tissue (50%), with a mean time to positive culture of 8.7 days. Early-onset infections were associated with increased mortality, particularly in patients with major burns, as five out of seven such individuals succumbed to infection. The overall mortality rate was 23%, despite timely antibiotic administration. Targeted topical antimicrobials, such as Dakin's solution, nanocrystalline silver, and polyhexamethylene biguanide, offer potential benefits but lack robust evidence for optimal use. Stronger clinical data are needed to guide their application and improve outcomes. These findings underscore the need for enhanced surveillance, refined treatment strategies, and research into S. marcescens management in burn care.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".