Prevention of chronic rhinosinusitis
Bibliographic record
Abstract
Prevention of chronicity of disease and minimising its impact with individualized treatment is a fundamental tenet of precision medicine. A review of the literature has been undertaken to explore how this may apply to chronic rhinosinusitis (CRS). Prevention may be thought of across 3 main domains. Primary prevention of CRS focuses on the avoidance of exposure to environmental factors associated with increased incidence of disease. This includes avoidance of tobacco smoke and occupational toxins. Although allergic rhinitis, respiratory infections and gastro-oesophageal reflux have been shown to be risk factors, there is no evidence as yet that treatment of these conditions is associated with reduced incidence of CRS. Secondary prevention of CRS is concerned with detecting a disease in its earliest stages, intervening to achieve disease and symptom control and preventing future exacerbations. Evidence based guidelines facilitate early diagnosis and appropriate use of medical and surgical interventions. In the future the use of endotypes to direct optimal is like to allow more clinically and cost-effective use of current and emerging treatments, such as monoclonal antibodies. Tertiary prevention aims to minimise the impact of an ongoing illness or injury that has lasting effects. Anxiety and depression have been shown to be associated with symptom amplification and may require treatment. The role of disease-related factors such as the role of the microbiome and osteo-neogenesis in the development of chronicity, and the development of severe combined upper airway disease needs further research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".