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Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Biospecimens and Biomarkers
Vishank Shah, Holly E. Hinson, Michael Reznik, Cecil D. Hahn, Sheila Alexander, Jonathan Elmer, Sherry H-Y Chou, Venkatesh Aiyagari, Yama Akbari, Fawaz Al‐Mufti, Anne W. Alexandrov, Ayham Alkhachroum, Moshagan Amiri, Brian Appavu, Meron Awraris Gebre, Mary Kay Bader, Neeraj Badjiata, Ram Balu, Megan E. Barra, Rachel Beekman, Ettore Beghi, Kathleen Bell, Erta Beqiri, Tracey Berlin, Thomas P. Bleck, Yelena Bodien, Varina L. Boerwinkle, Mélanie Boly, Alexandra Bonnel, Emery N. Brown, Eder Cáceres, Elizabeth Carroll, Emilio G. Cediel, Sherry Chou, Giuseppe Citerio, Jan Claassen, Chad Condie, Katie Cosmas, Claire J. Creutzfeldt, Neha Dangayach, Michael DeGeorgia, Caroline Der‐Nigoghossian, Masoom Desai, Michael N. Diringer, James Dullaway, Brian L. Edlow, Ari Ercole, Anna Estraneo, Guido J. Falcone, Salia Farrokh, Simona Ferioli, Davinia Fernández‐Espejo, Ericka L. Fink, Joseph J. Fins, Brandon Foreman, Jennifer Frontera, Rishi Ganesan, Ahmeneh Ghavam, Joseph T. Giacino, Christie Gibbons, Emily J. Gilmore, Olivia Gosseries, Theresa Green, David M. Greer, Mary Guanci, Ryan Hakimi, Flora M. Hammond, Daniel F. Hanley, Jed A. Hartings, Ahmed M. Hassan, Raimund Helbok, Claude Hemphill, Karen G. Hirsch, Sarah Hocker, Peter Hu, Xiao Hu, Theresa Human, David Y. Hwang, Judy Illes, Matthew Jaffa, Michael L. James, Anna Janas, Morgan Jones, E. Keller, Maggie Keogh, Jenn Kim, Keri S. Kim, Hannah Kirsch, Matt Kirschen, Nerissa Ko, Daniel Kondziella, Natalie Kreitzer, Julie Kromm, Abhay Kumar, Pedro Kurtz, Steven Laureys, Thomas Lawson, Nicolas Lejeune, Ariane Lewis, John Liang, Geoffrey Ling, Sarah Livesay, Andrea I. Luppi, Lori Madden, Craig Maddux, Dea Mahanes, Shraddha Mainali, Nelson Maldonado, Rennan Martins Ribeiro, Marcello Massimini, Stephan A. Mayer, Victoria McCredie, Molly McNett, Jorge Mejía-Mantilla, David Menon, Geert Meyfroidt, Julio Mijangos, Dick Moberg, Asma Moheet, Erika Molteni, Martin M. Monti, Chris Morrison, Susanne Muehlschlegel, Brooke Murtaugh, Lionel Naccache, Masao Nagayama, Emerson Nairon, Girija Natarajan, Virginia Newcombe, Niklas Nielsen, Naomi Niznick, Filipa Noronha-Falcão, Paul Nyquist, DaiWai M. Olson, Marwan Othman, Adrian M. Owen, Llewellyn Padayachy, Soojin Park, Melissa Pergakis, Len Polizzotto, Nader Pouratian, Marilyn Price Spivack, Lara Prisco, J. Javier Provencio, Louis Puybasset, Chethan Rao, Lindsay Rasmussen, Verena Rass, Risa Richardson, Cássia Righy Shinotsuka, Chiara Robba, Courtney Robertson, Benjamin Rohaut, John D. Rolston, Mario Rosanova, Eric S. Rosenthal, Mary E. Russell, Gisele Sampaio Silva, Leandro Sanz, Simone Sarasso, Aarti Sarwal, Nicolas Schiff, Caroline Schnakers, David B. Seder, Amy Shapiro-Rosen, Angela Hays Shapshak, Kartavya Sharma, Tarek Sharshar, Lori Shutter, Jacobo Sitt, Beth S. Slomine, Peter Smielewski, Wade S. Smith, Emmanuel A. Stamatakis, Alexis Steinberg, Robert D. Stevens, José I. Suarez, Bethany L. Sussman, Shaurya Taran, Aurore Thibaut, Zachary Threlkeld, Lorenzo Tinti, Daniel Toker, Michel Torbey, Stephen Trevick, Alexis F. Turgeon, Andrew Udy, Panos Varelas, Paul Vespa, Walter Videtta, Henning U. Voss, Ford Vox, Amy K. Wagner, Mark S. Wainwright, John Whyte, Briana Witherspoon, Aleksandra Yakhind, Ross Zafonte, Darin B. Zahuranec, Chris Zammit, Bei Zhang, Wendy Ziai, Lara Zimmerman, Elizabeth Zink
Bibliographic record
VenueNeurocritical Care · 2023
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and Stroke
KeywordsMedicineGeneralizability theoryBiomarkerBiomarker discoveryPersistent vegetative stateMedical physicsIntensive care medicineMinimally conscious statePsychology
Abstract
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 distilled prediction
Teacher imitationNot 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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.809
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.292
GPT teacher head0.466
Teacher spread0.175 · 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 teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Citations9
Published2023
Admission routes1
Has abstractno