Cytodiagnosis of Supratentorial Hemangioblastoma
Bibliographic record
Abstract
Letters| February 15 2011 Cytodiagnosis of Supratentorial Hemangioblastoma Subject Area: Pathology and Cell Biology Julia Keith; Julia Keith From the Department of Pathology (Neuropathology), and Department of Clinical Neurological Sciences (Neurosurgery), University Hospital, London, Ontario, Canada. Search for other works by this author on: This Site PubMed Google Scholar Lee-Cyn Ang; Lee-Cyn Ang From the Department of Pathology (Neuropathology), and Department of Clinical Neurological Sciences (Neurosurgery), University Hospital, London, Ontario, Canada. Search for other works by this author on: This Site PubMed Google Scholar Joseph Megyesi Joseph Megyesi From the Department of Pathology (Neuropathology), and Department of Clinical Neurological Sciences (Neurosurgery), University Hospital, London, Ontario, Canada. Search for other works by this author on: This Site PubMed Google Scholar Acta Cytologica (2008) 52 (2): 260–263. https://doi.org/10.1159/000325496 Article history Published Online: February 15 2011 Content Tools Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Julia Keith, Lee-Cyn Ang, Joseph Megyesi; Cytodiagnosis of Supratentorial Hemangioblastoma. Acta Cytologica 1 April 2008; 52 (2): 260–263. https://doi.org/10.1159/000325496 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsActa Cytologica Search Advanced Search This content is only available via PDF. 2008Copyright / Drug Dosage / DisclaimerCopyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements. Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".