Insula and Olfaction: A Literature Review and Case Report
Bibliographic record
Abstract
(1) Background: It is well established that the insula is involved in olfaction, though its specific role in olfactory processing remains uncertain. In this paper, we first review the current literature on the insula and olfaction. Then, we describe the case of a 56-year-old man with a left insular cavernoma that caused olfactory disturbances. (2) Results: Structural neuroimaging studies suggest that insular gray matter volume is related to olfactory function, and functional neuroimaging shows that various types of stimuli lead to either lateralized or bilateral insular activations. Studies using electro-cortical stimulation reveal a specific region of the insular cortex, around the central insular sulcus, that could be related to unpleasant odor processing. Previous cases of insular lesions leading to olfactory disturbances suggest that left-sided insular lesions may more frequently lead to olfactory changes. In our patient with a left insular cavernoma, odors that were previously perceived as pleasant started smelling unpleasant and were hard to distinguish. Despite these subjective complaints, olfactory function assessed with the Sniffin' Sticks test was normal. (3) Conclusions: Current tests may not be sensitive to all types of olfactory impairments associated with insular damage, and further studies should be conducted to develop olfactory tests assessing the hedonic appreciation of odors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".