Canadian clinical practice guidelines for acute and chronic rhinosinusitis
Bibliographic record
Abstract
This document provides healthcare practitioners with information regarding the management of acute rhinosinusitis (ARS) and chronic rhinosinusitis (CRS) to enable them to better meet the needs of this patient population. These guidelines describe controversies in the management of acute bacterial rhinosinusitis (ABRS) and include recommendations that take into account changes in the bacteriologic landscape. Recent guidelines in ABRS have been released by American and European groups as recently as 2007, but these are either limited in their coverage of the subject of CRS, do not follow an evidence-based strategy, or omit relevant stakeholders in guidelines development, and do not address the particulars of the Canadian healthcare environment.Advances in understanding the pathophysiology of CRS, along with the development of appropriate therapeutic strategies, have improved outcomes for patients with CRS. CRS now affects large numbers of patients globally and primary care practitioners are confronted by this disease on a daily basis. Although initially considered a chronic bacterial infection, CRS is now recognized as having multiple distinct components (eg, infection, inflammation), which have led to changes in therapeutic approaches (eg, increased use of corticosteroids). The role of bacteria in the persistence of chronic infections, and the roles of surgical and medical management are evolving. Although evidence is limited, guidance for managing patients with CRS would help practitioners less experienced in this area offer rational care. It is no longer reasonable to manage CRS as a prolonged version of ARS, but rather, specific therapeutic strategies adapted to pathogenesis must be developed and diffused.Guidelines must take into account all available evidence and incorporate these in an unbiased fashion into management recommendations based on the quality of evidence, therapeutic benefit, and risks incurred. This document is focused on readability rather than completeness, yet covers relevant information, offers summaries of areas where considerable evidence exists, and provides recommendations with an assessment of strength of the evidence base and degree of endorsement by the multidisciplinary expert group preparing the document.These guidelines have been copublished in both Allergy, Asthma & Clinical Immunology and the Journal of Otolaryngology-Head and Neck Surgery.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".