The role of biofilms in otolaryngologic infections
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: Bacterial biofilms have recently been shown to be important in diseases of the head and neck. Because the concept of biofilms is novel to most practitioners, it is important to gain a basic understanding of biofilms and to recognize that strategies developed to treat planktonic bacteria are ineffective against bacteria in a biofilm. RECENT FINDINGS: Bacteria preferentially exist in complex, surface-attached organizations known as biofilms. Bacteria in biofilms express a different set of genes than their planktonic counterparts and have markedly different phenotypes. Biofilm bacteria communicate with each other, and have mechanisms to diffuse nutrients and dispose of waste. Biofilms provide bacteria with distinct advantages, including antimicrobial resistance and protection from host defenses. Thus, bacteria exist in a far more complex fashion than previously thought and can best be thought of as "self-assembling multicellular communities." Although a focus on the planktonic form of bacteria has been useful in understanding acute infections, chronic infections are much better understood as biofilm illnesses. Biofilms have been shown to be involved in chronic otitis media, chronic tonsillitis, cholesteatoma, and device-associated infections. SUMMARY: Now that basic research has demonstrated that the vast majority of bacteria exist in biofilms, the biofilm concept of disease is beginning to spread throughout the clinical world. Understanding that many of the infections that affect structures of the head and neck are actually biofilm related is fundamental to developing rational strategies for treatment and prevention.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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