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Record W3121501086 · doi:10.2139/ssrn.3371405

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

2019· article· en· W3121501086 on OpenAlexaff
F. Kyle Satterstrom, Jack A. Kosmicki, Jiebiao Wang, Michael S. Breen, Silvia De Rubeis, Joon‐Yong An, Minshi Peng, Ryan L. Collins, Jakob Grove, Lambertus Klei, Christine Stevens, Jennifer Reichert, Maureen Mulhern, Mykyta Artomov, Sherif Gerges, Brooke Sheppard, Xinyi Xu, Aparna Bhaduri, Utku Norman, Harrison Brand, Grace Schwartz, Rachel Nguyen, Elizabeth E. Guerrero, Caroline Dias, Branko Aleksić, Mafalda Barbosa, Somer Bishop, Alfredo Brusco, Jonas Bybjerg‐Grauholm, Ángel Carracedo, Marcus C.Y. Chan, Andreas G. Chiocchetti, Brian Hon‐Yin Chung, Hilary Coon, Michael L. Cuccaro, Bernardo Dalla Bernardina, Ryan N. Doan, Enrico Domenici, Chiara Fallerini, Giovanni Battista Ferrero, Christine M. Freitag, Menachem Fromer, J. Jay Gargus, Elisa Giorgio, Stephen J. Guter, Danielle Halpern, Emily Hansen‐Kiss, Xin He, Gail E. Herman, Irva Hertz‐Picciotto, David M. Hougaard, Christina M. Hultman, Iuliana Ionita‐Laza, Suma Jacob, Jesslyn Jamison, Astanand Jugessur, Miia Kaartinen, Gun Peggy Knudsen, Alexander Kolevzon, Itaru Kushima, So Lun Lee, Elaine T. Lim, Carla Lintas, W. Ian Lipkin, Diego Lopergolo, Yunin Ludeña, Patrı́cia Maciel, Per Magnus, Behrang Mahjani, Nell Maltman, Dara S. Manoach, Gal Meiri, Idan Menashe, Judith Miller, Nancy J. Minshew, Eduarda Montenegro M. de Souza, Danielle de Paula Moreira, Eric M. Morrow, Ole Mors, Preben Bo Mortensen, Matthew W. Mosconi, Pierandrea Muglia, Benjamin M. Neale, Merete Nordentoft, Norio Ozaki, Aarno Palotie, Mara Parellada, Maria Rita Passos‐Bueno, Margaret A. Pericak‐Vance, Antonio M. Persico, Isaac N. Pessah, Kaija Puura, Abraham Reichenberg, Alessandra Renieri, Evelise Riber, Elise Robinson, Kaitlin E. Samocha, Sven Sandin, Susan L. Santangelo, Gerry Schellenberg, Stephen W. Scherer, Sabine Schlitt, Rebecca J. Schmidt, Lauren Schmitt, Isabela Maya W. Silva, Tarjinder Singh, Paige M. Siper, Moyra Smith, Gabriela Soares, Camilla Stoltenberg, Ezra Susser, John A. Sweeney, Péter Szatmári, Flora Tassone, Karoline Teufel, Elisabetta Trabetti, Maria del Pilar Trelles, Christopher A. Walsh, Lara Tang, Lauren A. Weiss, Thomas Werge, Donna M. Werling, Emilie M. Wigdor, Emma Wilkinson, A. Jeremy Willsey, Timothy W. Yu, Mullin H.C. Yu, Ryan K. C. Yuen, Elaine Cristina Zachi, Catalina Betancur, Edwin H. Cook, Louise Gallagher, Michael Gill, James S. Sutcliffe, Audrey Thurm, Michael E. Zwick, Matthew W. State, A. Ercüment Çiçek, Michael E. Talkowski, David J. Cutler, Bernie Devlin, Stephan Sanders, Kathryn Roeder, Mark J. Daly

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsGenome CanadaMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsExome sequencingAutismAutism spectrum disorderBiologyCopy-number variationPhenotypeGeneNeurodevelopmental disorderGeneticsNeuroscienceExomePsychologyGenomeDevelopmental psychology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations32
Published2019
Admission routes1
Has abstractno

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