Bibliographic record
Abstract
In this issue, Williams et al. provide new and very interesting information on the molecular tumorigenesis of a substantial subset of sporadic schwannomas.1 Before discussing the results of this study in somewhat more detail, it may be good to briefly look back at the genesis of the concept of schwannomas. It was Antoni van Leeuwenhoek, a multitalented Dutch microbiologist, who discovered the myelination of nerve fibers in 1717. More than a century later, the German anatomist and physiologist Theodor Schwann suggested the association between myelin and the “lemmocyte,” a cell type that later on became known as Schwann cell.2,3 The term “schwannoma” was coined by the French Canadian histopathologist Pierre Masson in 1923, and Jose Juan Verocay, a Uruguayan neuropathologist, played an important role in the more precise description of these tumors.4 The architectural pattern of alternating cellular areas with nuclear palisading including Verocay bodies (Antoni A) and loosely organized areas with myxomatous and cystic changes (Antoni B) was documented in 1920 by the Swedish neurologist Nils Antoni (Figure 1).5
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".