Bibliographic record
Abstract
PURPOSE OF REVIEW: In contrast to the phenotypic classification of chronic rhinosinusitis (CRS), endotyping categorizes disease variants based on their underlying pathophysiologic mechanisms. Defining CRS endotypes may provide information on the risk for disease progression, recurrence and comorbid conditions, as well as identify suitable therapeutic targets. With the emergence of biologics, endotyping may enable personalized pharmacotherapy for recalcitrant CRS. The purpose of this review is to briefly summarize the pathophysiology and endotypes of CRS, and highlight the biologics that target mediators of CRS. RECENT FINDINGS: CRS is due to dysregulated immunologic responses to external stimuli, which induces inflammatory mediators. The linkage between innate lymphoid cells, adaptive CD4 T helper and CD8 + cytotoxic T cells has led to proposed endotypes that are based around immune response deviation into type 1, type 2 and type 3 responses. Cluster analysis has attempted to define endotypes, accounting for clinical characteristics, molecular and cellular biomarkers, and treatment response. Biologics targeting epithelial-derived cytokines and immunoglobulin E, as well as mediators of type 1, type 2 and type 3 inflammation, are being investigated in CRS. SUMMARY: Although there have been significant advances made in the understanding of the pathomechanisms of CRS, there currently remains a lack of full characterization of CRS endotypes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".