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Databases for Congenital Heart Defect Public Health Studies Across the Lifespan
Tiffany Riehle‐Colarusso, Lisa Bergersen, Craig S. Broberg, Cynthia H. Cassell, Darryl T. Gray, Scott D. Grosse, Jeffrey P. Jacobs, Marshall L. Jacobs, Russell S. Kirby, Lazaros Kochilas, Asha Krishnaswamy, Arianne Marelli, Sara K. Pasquali, Thalia Wood, Matthew E. Oster, Ginnie Lee Abarbanell, Faith Adams, S. W. Allen, Sydney Allen, Anand Ambrose, Carl L. Backer, Andrea Baer, Carissa M. Baker‐Smith, Mona Barmash, Amy Basken, Cassandra Bates, Sarosh P. Batlivala, Robert H. Beekman, John W. Belmont, Joshua Benke, Stuart Berger, JR Bockerstette, Jeffrey R. Boris, Lorenzo D. Botto, Jackie L. Boucher, Dana Brock Hageman, Cheryl Brosig Soto, Kristin Marie Burns, Lenore Cameron, Robert M. Campbell, Steven E. Colan, Lynn Colegrove, Christina Coleman, Angie Colson, Adolfo Correa, Pamela Costa, Chris Couser, Melissa Crenshaw, Tessa Crume, Rachel Daskalov, Mark Del Monte, Lindsay L. DeSantis, Kaitlin Doherty, Kenneth J. Dooley, Charles W Duke, Pirooz Eghtesady, Saiza Elayda, Alison Ellison, Tim Elsner, Cori Erntz, Michelle Esquivel, Bethany Evans, Lloyd R. Feit, Marcia L. Feldkamp, William J. Foley, Elyse Foster, Wayne Franklin, Bridget Freeley, Frank M. Galioto, Mary G. George, Michael H. Gewitz, Katja M. Gist, Thomas Glenn, Melissa Jill Glidewell, Lorraine A. Gore, Johanna M. Gray, Hannah Green, Michelle Gurvitz, Sonia Handa, Melissa Harvey, Emilie Heath, Danielle Hile, John S. Hokanson, Margaret A. Honein, Marius M. Hubbell, Jeff Hudson, Kelly Huhn, Dawn Ilardi, Dawn C. Jacobs, Robert D.B. Jaquiss, Kathy J. Jenkins, Anitha S. John, P. M. Johnson, Shakila Johnson, Emily Jones, Antonios P. Jossif, Jonathan R. Kaltman, David Kasnic, Alex R. Kemper, Natalie Kenny, Paul Khairy, Valerie King, Donna Knapp, Daisuke Kobayashi, Adrienne H. Kovacs, James E. Kucik, Karen Kuehl, Alexandra Kuznetsov, Scott Leezer, Jodi Lemacks, Patty Libby, Paul H. Lipkin, Michele A. Lloyd-Puryear, Keila N. Lopez, Nicolas Madsen, Cara Mai, Monica Mann, Bradley S. Marino, Gerard R. Martin, G. Paul Matherne, Phillip Mauller, Susan May, Edward R.B. McCabe, Nancy McCabe, Michelle McCardle, Ty McCathran, Amy McCathran, Michael E. McConnell, Kristine Brite McCormick, Eric Melsom, William Kelly Milionis, Paula G. Miller, Erika Miller, Stephanie Mitchell, Cynthia A. Moore, Laura Morris, Angela Murray, Kathleen Mussatto, Steven R. Neish, Sue Nelson, Jane W. Newburger, Jeremy Nicolarsen, Autumn Niggles, Jacqueline A. Noonan, Gail Ober, Lori O’Keefe, Marc Overcash, Jennifer Page, Matthew Vaughn Park, Mehul Patel, Jasmin Patel, Gail D. Pearson, Cindy Pellegrini, Corrie Pierce, Nelangi M. Pinto, Kara D. Polen, Jose A. Quinones, Carol Raimondi, P. Richter, Michelle Rintamaki, Elisa Robles, Geoffrey L. Rosenthal, Grahame Rush, Laura Russell, Annamarie Saarinen, Craig Sable, Joel Saltz, Terri Schaefer, Kathryn Schubert, Vida Schwartz, Stuart K. Shapira, Kathleen M. Sheehan, Brenda Silverman, Regina M. Simeone, Juanita Smith, Kimberly E. Smith, Kristina Smith, Marci K. Sontag, Shubhika Srivastava, Corrie Stassen, Corey Stiver, Kathryn A. Taubert, Judy Thibadeau, John P. Thomas, Dena Thomas, Vivian Thorne, Linda Bradley Tiernan, S. M. Timmins, Colby Tiner, Natalie Torentinos, Glenn R. Tringali, James S. Tweddell, Lisa Vasquez, Amy Verstappen, Janice Ware, Caron Watkins, Catherine L. Webb, Ellen Weiss, Marina Weiss, Gil Wernovsky, Gretchen Whitehurst, Herbert Whitley, Jennifer Witten, Austin Henry Wong, Matthew Wright, Robert Wynbrant, Bistra Zheleva
Bibliographic record
VenueJournal of the American Heart Association · 2016
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMontreal Heart Institute
FundersNational Institute of Nursing ResearchNational Heart, Lung, and Blood Institute
KeywordsDatabasePublic healthMedicineComputer scienceNursing
Abstract
Peer Reviewed
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.
metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.044
Threshold uncertainty score0.105
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.044 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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.
Opus teacher head0.139
GPT teacher head0.457
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview
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
Citations33
Published2016
Admission routes1
Has abstractyes