Evaluating the clinical usefulness of structured questions in parosmia assessment
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Parosmia and phantosmia relate to distorted odor perceptions. Little is known about their clinical significance. Measuring phantosmia and parosmia is still not possible. Today, assessment of parosmia or phantosmia relies mainly upon the patient's interview and the physician's experience. Therefore, we investigated the clinical usefulness of four structured questions in comparison to the patient's history regarding their accuracy in terms of the presence of odor distortions. STUDY DESIGN: Tertiary care center outpatient clinic analyses. METHODS: Responses from 193 patients were analyzed. All patients underwent full olfactory work-up (ear, nose, and throat examination, Sniffin' Sticks testing, structural brain imaging) and filled in a questionnaire with four parosmia questions and six questions regarding characteristics and severity of the parosmia. These responses formed the bases of a numerical parosmia score. RESULTS: Patients with parosmia showed significantly lower parosmia scores (P <.001) when compared to either patients with phantosmia or patients without odor distortions. Two questions could be identified that showed a high association to the presence or absence of parosmia. CONCLUSIONS: The present results confirm reports on the high frequency of parosmia and phantosmia among patients suffering from olfactory disorders. A parosmia score could be established that distinguishes between patients with or without odor distortions. The score provides valuable information regarding the presence or absence of parosmia, thus helping the physician during the patient's evaluation.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".