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Record W4401365038 · doi:10.3233/npm-249005

Proceedings of the 15 <sup>th</sup> International Newborn Brain Conference: Neuro-imaging studies

2024· article· en· W4401365038 on OpenAlexaffabout
AbdulAziz Al-Garni, Saima Aslam, Zarina Assis, Karman Avanaki, Maria Chiara Bagnato, Alan Bainbridge, Kelly Pegoretti Baruteau, Renzo Beghini, Juliana Benavides, Amina Benlamri, Angelika Berger, Megan Ní Bhroin, Francesca Bissolo, Arun L.W. Bokde, Elena Bonafiglia, Martijn F. Boomsma, Vivanne Boswinkel, Geraldine B. Boylan, Julia Buchmayer, Joe Bwambale, Angela Byrne, Fady T. Charbel, Sara Cherkerzian, Gabrielle Colleran, Gabriel Côté‐Corriveau, Frances M. Cowan, Anais Durand, Mohamed El‐Dib, Hoda El-Shibiny, Alia Embaireeg, Carmina Erdei, Hugo Alexandre Ferreira, Elsa Fiedrich, Carlo Alberto Forcellini, Rossella Frassoldati, Renate Fuiko, Aisling A. Garvey, Juri G. Gelovani, Gilbert Gilbert, Katharina Goeral, Ipsita Goswami, P. Ellen Grant, Laila Hadava, Mimily Harsono, Misa Hashimoto, Lena Hellström‐Westas, Leonora Hendson, Emma Hofland-Burry, Tim Hurley, Terrie E. Inder, Tarikul Islam, Yuji Ito, Camilo Jaimes, Raphaela JernejGregor Kasprian, Michael Kawooya, Lynne Kelly, Hiroyuki Kidokoro, Patric Kienast, Regan King, Sumire Kumai, Alexander Leemans, Lara M. Leijser, Marguerite Leoni, Jun Li, Samson K. Lubowa, Takashi Maeda, Pradeep Mally, Ivan Mambule, Rayyan Manwar, Nuno Matela, Sean Mathieson, Avneet Mazara, Laura Stone McGuire, Gerda Meijler, Martha Menchaca, Hélène Meunier, Takamasa Mitsumatsu, Khorshid Mohammed, Eleanor J. Molloy, Sarfaraz Momin, Nuno Canto Moreira, Jamiir Mugalu, Prashanth Murthy, Allena Nabawanuka, Tomohiko Nakata, Annettee Nakimuli, Carol Nanyunja, Hajime Narita, Nima Naseh, Jun Natsume, Chiara Nosarti, Tatiana Nuzum, Moffat Nyirenda, Mary O’Dea, Laura Pecoraro, Sofia Pellizzari, De‐Ann M. Pillers, Massroor Pourcyrous, Divya Rana, Nicola J. Robertson, Sriya Roychaudhuri, Samantha Sadoo, Yoshiaki Sato, Fumi Sawamura, Jeanne Scotland, James Scott, Danielle Sharon, Anna Shiraki, Anthony Shoo, Amanda P. Siegel, Elizabeth Singh, Marie Slevin, Latha Srinivasan, Huzair Ssesembo, Nina Stein, Sophie Stummer, Ryosuke Suzui, Deirdre Sweetman, Enikő Szakmár, Gentaro Taga, Selphee Tang, Cally J Tann, Chantal M. W. Tax, Francisco Torrealdea, Lucy Vanes, Tânia F. Vaz, Sujith Gurram Venkata, Mariela Adriana Ventola, Anouk S. Verschuur, Elena V. Wachtel, Brian H. Walsh, Hama Watanabe, Emily L. Webb, Gerda van Wezel‐Meijler, Pia Wintermark, Misae Yamada, Hiroyuki Yamamoto, Hussein Zein, Arianna Zuccato

Bibliographic record

VenueJournal of Neonatal-Perinatal Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsAlberta Children's HospitalHamilton Health SciencesUniversity of British ColumbiaSurrey Memorial HospitalCentre Hospitalier Universitaire Sainte-JustineMcMaster Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsNeuroimagingMedicineEncephalopathyMagnetic resonance imagingHypothermiaHypoxic Ischemic EncephalopathyNeonatal encephalopathyPediatricsIntensive care medicineInternal medicineRadiologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Hypoxic Ischemic Encephalopathy (HIE) is a signifi cant cause of perinatal encephalopathy, affecting a substantial number of neonates.Therapeutic hypothermia (TH) has become a standard treatment, signifi cantly reducing mortality leaving a portion of survivors with varying degrees of neurodevelopmental impairment.Neuroimaging, specifi cally magnetic resonance imaging (MRI), plays a crucial role in prognostication.A standardized MRI scoring system to assess brain injury severity and extent is crucial for predicting long term neurodevelopmental outcome. OBJECTIVES:This study aimed to investigate the predictive value of the Canadian Standardized consensus classifi cation of brain injury diagnosed with MRI on long -term neurodevelopmental outcomes at 18 -24 months of corrected age in infants with HIE post therapeutic hypothermia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.313
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

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