Microbial Biofilms in Pulmonary and Critical Care Diseases
Bibliographic record
Abstract
Microbial biofilms can colonize medical devices and human tissues, and their role in microbial pathogenesis is now well established. Not only are biofilms ubiquitous in natural and human-made environments, but they are also estimated to be associated with approximately two-thirds of nosocomial infections. This multicellular aggregated form of microbial growth confers a remarkable resistance to killing by antimicrobials and host defenses, leading biofilms to cause a wide range of subacute or chronic infections that are difficult to eradicate. We have gained tremendous knowledge on the molecular, genetic, microbiological, and biophysical processes involved in biofilm formation. These insights now shape our understanding, diagnosis, and management of many infectious diseases and direct the development of novel antimicrobial therapies that target biofilms. Bacterial and fungal biofilms play an important role in a range of diseases in pulmonary and critical care medicine, most importantly catheter-associated infections, ventilator-associated pneumonia, chronic Pseudomonas aeruginosa infections in cystic fibrosis lung disease, and Aspergillus fumigatus pulmonary infections.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".