Bibliographic record
Abstract
Longtemps considere comme un sens peu important, l’odorat suscite depuis quelques annees un interet croissant. Notamment, de plus en plus de cliniciens sont persuades de l’importance d’une prise en charge correcte des patients presentant des troubles de l’odorat. En outre, diverses etudes ont montre que les troubles de l’odorat sont bien plus frequents au sein de la population que ce qu’on imagine. De plus, les pathologies naso-sinusiennes chroniques s’accompagnent frequemment de troubles de l’odorat. Il est donc courant en pratique ORL de rencontrer des patients ayant des plaintes olfactives. Une prise en charge adequate de ces patients est necessaire pour etablir un diagnostic etiologique et ainsi proposer un eventuel traitement et fournir une information complete au patient. La comprehension des mecanismes physiologiques a la base de la perception d’un message olfactif ainsi que les explorations fonctionnelles chez un patient presentant un trouble de l’odorat ont fortement evolue ces dernieres annee. Cet article permet de realiser une mise a jour des connaissances sur la physiologie de l’olfaction et insiste sur les explorations fonctionnelles actuelles qui sont a disposition du clinicien pour diagnostiquer de la maniere la plus precise un trouble olfactif chez un patient
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.977 | 0.979 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".