Cancer neurology in clinical practice, neurological complications of cancer and its treatment (2nd Edn)
Bibliographic record
Abstract
Santosh Kesari, David Schiff and Patrick Wen have released the second edition of their book ‘cancer neurology in clinical practice’. The book addresses the neurological impact of cancer and cancer treatment. Neurological implications of cancers of the central and peripheral nervous system, as well as cancers of other systems are described in well-written and well-organised chapters. The scope of this book is indeed unique as it offers a comprehensive view on topics forming a common denominator to both neurology and oncology, but that are too often neglected in classical textbooks of either disciplines. However, as neuro-oncology is becoming a prominent and true multidisciplinary specialty, there is a need for such a multidisciplinary book addressing practical issues that are encountered every day in the clinical oncological practice. Moreover, as a high number of oncology patients with cancers outside of the nervous system develop neurological symptoms such as pain, headaches, weakness, cognitive disturbances or paraneoplastic syndromes, even the medical oncologist not dealing primarily with brain tumours will quite often be confronted with neurological problems in his or her patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.028 |
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".