Bibliographic record
Abstract
I, as a neuroradiologist and member of the NASBS, attended and enjoyed the recent combined gathering of the 19th Annual Meeting of the North American Skull Base Society and the 5th International Skull Base Congress of the World Federation of Skull Base Societies in Vancouver Canada. I never cease to be amazed by the skill of my surgical colleagues and their grasp of complex anatomy (why can't the various vascular and neuronal structures be color-coded like Netter, rather than a monotonous red tone?). I think it is important for neuroradiologists to attend such meetings to better appreciate the surgical approaches and applied anatomy of skull base neoplasms for improved image interpretation. However, as a former radio host once said, “words do have meaning.” Why then the constant reference to “acoustic neuromas,” when we all know these same tumors are not neuromas at all ( neuron , Greek for nerve, and oma , Greek for swelling), but tumors of Schwann cells, and thus schwannomas? Further, less than 5% of schwannomas of the internal auditory canal derive from the acoustic division, but rather overwhelmingly originate from either the superior or inferior vestibular nerves. The exact origin of these lesions, as described in the literature, is confusing, ranging from predominantly superior vestibular schwannomas to predominantly inferior vestibular schwannomas (see a good summary article by Komatsuzaki A and Tsunoda A, J Laryngol Otol, May 2001:376–379). Further, with thin section MR imaging, you can see schwannomas in their early stages arising from the individual divisions of cranial nerve eight. At any rate, you would more often be right than wrong when calling these lesions vestibular schwannomas. The neurosciences are not alone in such confusion of terms. Morton's neuroma of the foot is not a neuroma at all but a fibrous swelling about the interdigital nerves of the foot. Further, Louis Durlacher (a chiropodist to the Queen of England) was the first to describe this condition in 1845, 31 years before the publication of Thomas G. Morton, a physician in Philadelphia who somehow aced out Durlacher. Well, so much for head-to-toe neuroscience. I would appeal to future speakers, as well as publication authors, to embrace the proper terminology. Learning the complex anatomy of the skull base is tough enough for residents and fellows in neurosurgery, otolaryngology, and neuroradiology, without having to deal with confusing nomenclature. Finally, I am sure Theodor Schwann (1810–1882) would be pleased to hear the proper description of these lesions that bear his name.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".