Bibliographic record
Abstract
Gaurav Medikeri,1 Amin Javer2 1Medikeri’s Superspeciality ENT Center, Bangalore, India; 2Rhinology & Skull Base Surgery, St. Paul’s Sinus Center, Vancouver, BC, CanadaCorrespondence: Amin JaverSt. Paul’s Sinus Centre, 1081 Burrard St, Vancouver BC V6Z 1Y6, CanadaTel +1- 604-806-9926Email sinusdoc@me.comIntroduction: Allergic fungal rhinosinusitis (AFRS) is a chronic disorder with significant morbidity and a high recurrence rate needing long-term follow-up. Even after its first description many decades ago, there is still considerable uncertainty about the management of this condition.Description: In this chapter, we breakdown the topic “Optimal management of allergic fungal rhinosinusitis” into sub-headings in order to discuss the latest research and available literature under each topic in great detail. Every attempt has been made to incorporate the highest level of evidence that was available at the time of writing.Summary: Pre-operative diagnosis and further management prior to surgery is important. Steroids help in reducing inflammation and help improve the surgical field. Surgery remains the mainstay in the management of this condition along with long-term medical management. Oral steroids are reserved for acute flare-ups in the background of associated lung concerns. Oral and topical antifungal agents have no role in the control of the disease. Biological agents are being prescribed predominantly by respiratory physician colleagues, mainly for the control of the chest-related issues rather than for sinus disease. Immunotherapy as an adjunct with surgery is promising.Conclusion: AFRS is a disease with many variables and a wide range of symptomatic presentation. It takes a keen clinician to identify the disease and subsequently manage the condition. Treatment involves long-term follow-up with early detection of recurrence or flare-ups. Any of the mentioned modalities of management may be employed to effectively control the condition, and treatment protocols will have to be tailor-made to suit each individual patient. Various medications and drugs such as Manuka honey, antimicrobial photodynamic therapy, hydrogen peroxide and betadine rinses appear to be promising. More robust studies need to be undertaken to ascertain their routine use in clinical practice.Keywords: fungal, allergic, rhinosinusitis, eosinophilic, IgE, immunotherapy
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".