An update on the classifications, diagnosis, and treatment of rhinosinusitis
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review is timely and relevant because rhinosinusitis is a disease process that is heterogeneous in its clinical and pathologic manifestations. Therefore, no one causative factor has been identified that fully accounts for all rhinosinusitis. The purpose of this review is to provide a succinct update of rhinosinusitis classification, pathophysiology, and management given the new movement toward evidence-based guidelines. RECENT FINDINGS: The term rhinosinusitis reflects the concurrent inflammatory and infectious processes that affect the nasal passages and the contiguous paranasal sinuses. The most recent classification scheme is intended primarily to guide clinical research and divides rhinosinusitis into four categories: acute bacterial rhinosinusitis, chronic sinusitis with nasal polyposis, chronic rhinosinusitis with nasal polyposis, and allergic fungal rhinosinusitis. The goals of treatment include reduction of mucosal edema, reestablishment of sinus ventilation, and eradication of infecting pathogens. Multiple therapies are available for the management of chronic rhinosinusitis, including antibiotics, hypertonic and isotonic saline irrigations or sprays, topical and systemic glucocorticords, antileukotriene agents, and endoscopic sinus surgery. SUMMARY: Rhinosinusitis is a common medical problem that interferes with patient quality of life and loss of work productivity. Because of the heterogeneity that underlies its pathology, no one treatment regimen exists for the management of rhinosinusitis.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".