Meta-Analysis of Neurodevelopmental Outcome after Room Air versus 100% Oxygen Resuscitation: Generating More Questions than Answers
Bibliographic record
Abstract
Commentaries| June 06 2012 Meta-Analysis of Neurodevelopmental Outcome after Room Air versus 100% Oxygen Resuscitation: Generating More Questions than Answers?: Commentary on O.D. Saugstad et al.: Neurodevelopmental Outcome of Infants Resuscitated with Air or 100% Oxygen: A Systematic Review and Meta-Analysis (Neonatology 2012;102:98–103) Subject Area: Women's and Children's Health Prakesh S. Shah Prakesh S. Shah aDepartment of Paediatrics, Mount Sinai Hospital and University of Toronto, and bMaternal-Infant Care Research Centre, Mount Sinai Hospital, Toronto, Ont., Canada Search for other works by this author on: This Site PubMed Google Scholar Neonatology (2012) 102 (2): 104–106. https://doi.org/10.1159/000334226 Article history Received: October 06 2011 Accepted: October 06 2011 Published Online: June 06 2012 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 Prakesh S. Shah; Meta-Analysis of Neurodevelopmental Outcome after Room Air versus 100% Oxygen Resuscitation: Generating More Questions than Answers?: Commentary on O.D. Saugstad et al.: Neurodevelopmental Outcome of Infants Resuscitated with Air or 100% Oxygen: A Systematic Review and Meta-Analysis (Neonatology 2012;102:98–103). Neonatology 1 August 2012; 102 (2): 104–106. https://doi.org/10.1159/000334226 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 JournalsNeonatology Search Advanced Search Article PDF first page preview Close Modal This content is only available via PDF. © 2012 S. Karger AG, Basel2012Copyright / 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. You do not currently have access to this content.
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 | 0.101 | 0.458 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.017 | 0.011 |
| 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".