Bibliographic record
Abstract
Chronic rhinosinusitis (CRS) is a heterogenous airways disease characterised by inflammation of the upper airways and sinuses that persists for at least 12 weeks. CRS is frequently divided into two endotypes based on the presence or absence of nasal polyps (NPs): CRS with NPs (CRSwNP) and CRS without NPs (CRSsNP). Inflammatory patterns in the two groups are different and studies indicate that CRSsNP is dominated by a type 1 inflammatory profile associated with increased interferon (INF)-γ and tumour necrosis factor (TGF)-β expression and neutrophilic inflammation.1 By comparison, type 2 inflammatory responses characterised by interleukin (IL) 4, IL-5, IL-13 cytokine expression and eosinophilic inflammation are features of CRSwNP.1 This categorisation may, however, be oversimplified as recent studies show that this may be subject to racial and regional differences.2 ,3 While the majority of Caucasian patients sampled in the USA and Europe have a pronounced infiltration of eosinophils and expression of IL-5 in the NPs, patients with CRSwNP in East Asian countries including Japan, Korea and China, exhibit a mixed inflammatory profile. About half the Asian patients with CRSwNP exhibit an eosinophilic chronic rhinosinusitis (ECRS) while the rest have a non-eosinophilic more neutrophilic or mixed inflammatory chronic rhinosinusitis (non-ECRS) characterised by type 1 dominant inflammation. Although a number of hypotheses have been proposed regarding the pathogenesis of CRSwNP, the precise molecular mechanisms remain unclear and likely lie beyond rudimentary clinical classifications of CRS. An emphasis on defining CRS endotypes based on distinct functional or pathobiological mechanisms of disease may be more effective at identifying patient groups that would respond to therapeutic interventions targeting specific proinflammatory mediators or infectious agents that trigger the disease pathology. The inflammatory profile of CRSsNP is largely type 1, although there are discrepancies over which isoforms of TGF-β are increased per se and in what …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".