European position paper on diagnostic tools in rhinology
Bibliographic record
Abstract
The accurate diagnosis of rhinologic disease depends on the clinical history, examination findings and, in many cases, further investigations. There are a wide variety of diagnostic tests available, the choice of which depends upon the condition being assessed. This position paper is intended to provide an up-to-date comprehensive description of the diagnostic tools available to rhinologists, allergists, general otolaryngologists and other physicians with an interest in sinonasal disease. The literature has been reviewed and evidence-based recommendations are included. The relevant history and examination techniques are described, including endoscopic assessment of the nose. General and disease-specific quality of life instruments are an important tool in assessing the impact of rhinologic disease and the response to treatment. Relevant blood tests are discussed, as well as the various methods of allergy testing. Techniques for collecting microbiological and tissue samples are described, as well as the use of more specialised tests such as nasal nitric oxide and those evaluating ciliary structure and function. Imaging techniques and their indications are included. Chemosensory (smell and taste) testing is explained, and the available techniques for objective measurement of nasal airflow and patency are reviewed. Prompt and accurate diagnosis allows appropriate management to be initiated; an understanding of the currently available diagnostic tools is a vital part of the assessment of rhinologic disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".