Clinical Biofilms: A Challenging Frontier in Wound Care
Bibliographic record
Abstract
Significance: Biofilms have been implicated in a variety of wound complications. Recent Advances: Research has confirmed that biofilms form in wounds of patients experiencing delayed healing and may be a precursor to infection. Critical Issues: Research into the strength of this association is still in its infancy. Is biofilm formation a cause of these complications, a step toward them, or a signal that unresolved factors injuring tissue or delaying healing are setting the stage for biofilm formation, infection, and healing delay? To qualify biofilms for use in informing clinical practice decisions, biofilm characteristics supporting those decisions need standardized definitions and valid evidence that they predict or diagnose healing or infection outcomes. Literature searches of relevant terms reviewed biofilm definitions and validation of their role in predicting and diagnosing delayed wound healing or infection. Future Directions: Further research is needed to provide a rapid accurate technique to identify and characterize biofilms in ways that optimize their validity in diagnosing or screening patient risk of infection or delayed healing and to inform clinical decisions. This research will help validate biofilm's capacity to support wound care clinical practice decisions and establish their importance in guiding clinical practice.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".