Bibliographic record
Abstract
By contrast with the smoked drum and other mechanical systems, modern electronic rhinomanometers provide superior sensitivity and frequency response. They enable accurate measurement of nasal airflow resistance to be made and are commercially available. In addition to a rhinomanometer, nasal airflow measurements require a face mask fitted with a flow measuring device (a pneumotach connected to an electronic differential pressure transducer) or, as an alternative to a face mask, a head-out body plethysmograph. Concurrently with nasal respiratory airflow, transnasal pressures between the nostril and pharynx are measured via nasal or oral tubing by a second differential pressure transducer. The transduced electronic analogue signals are digitized, and nasal airflow resistances are computed from the ratio between transnasal pressure and airflow. At a single sitting, a series of measurements with modern rhinomanometry can determine (1) the response to topical decongestant of the mucovascular contribution to nasal airflow resistance at the time of examination and (2) in the decongested nose, the presence, side, site, and severity of structural obstruction. Rhinomanometry is not "medically necessary" in assessment of all cases of nasal obstructive symptoms, but, in many situations, it can provide valuable objective information in compliance with the requirements of evidence-based medicine. This article includes a table listing situations in which rhinomanometry is particularly useful.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".