Microbial Biofilms in Pulmonary and Critical Care Diseases
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
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.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it