Bibliographic record
Abstract
I will be honest.I had not expected to like this book.At approximately 570 pages of text, it sits between small books such as Escourolle and Poirier [1], Adams and Graham's [2] and of course the WHO Tumour Classification [3], and reference texts such as Ellison and Love [4], and Greenfield [5]. Neuropathology is a small discipline with a reasonable number of well-written specialist texts, and sections in books with wider remit.I was not sure that there was a need for an 'in-betweener'.I was quickly proved wrong.This book is different because of its 'pattern-based' approach.The opening chapter is key in attempting to teach the reader, rather than requiring the reader to read and understand.For me, this leads to a deeper level of learning, and therefore I think the book is particularly valuable for neuropathologists in training and histopathologists who are interested in neuropathology.The editors have a self-professed commitment to education and are internationally renowned neuropathologists, and the generosity of knowledge in this book is clear.There are 28 contributors, from the USA, Canada, France, Germany and Portugal.The entire book is very well illustrated, with large good-quality colour images of macroscopic findings, histology, immunohistochemistry and radiology.Images are also included of important molecular tests, for example, dual-colour fluorescence in situ hybridization, and there are a few electron microscopy images.Coloured headers
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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".