Proceedings from the Consensus Conference on Trauma Patient-Reported Outcome Measures
Bibliographic record
Abstract
Sakran, Joseph V. MD, MPH, MPA, FACS; Ezzeddine, Hiba MD; Schwab, William C. MD, FACS; Bonne, Stephanie MD, FACS; Brasel, Karen J. MD, MPH, FACS; Burd, Randall S. MD, PhD, FACS; Cuschieri, Joseph MD, FACS; Ficke, James MD, FACS; Gaines, Barbara A. MD, FACS; Giacino, Joseph T. PhD; Gibran, Nicole S. MD, FACS; Haider, Adil MD, MPH, FACS; Hall, Erin C. MD, FACS; Herrera-Escobar, Juan P. MD, MPH; Joseph, Bellal MD, FACS; Kao, Lillian MD, FACS; Kurowski, Brad G. MD, MS, FACS; Livingston, David MD, FACS; Mandell, Samuel P. MD, MPH, FACS; Nehra, Deepika MD, FACS; Sarani, Babak MD, FACS; Seamon, Mark MD, FACS; Yonclas, Peter MD; Zarzaur, Ben MD, MPH, FACS; Stewart, Ronald MD, FACS; Bulger, Eileen MD, FACS; Nathens, Avery B. MD, MPH, PhD, FACS; Amtmann, Dagmar PhD; Bixby, Pam; Brighton, Brian MD, MPH; Burstin, Helen MD, MPH; Burns, Chris MD, FACS; Caldwell, Michelle; Chaney, Eric; Chung, Kevin MD, FCCM, FACP; Cipolle, Mark MD, PhD, MS; deRoon-Cassine, Terri MS, PhD; Dicker, Rochelle MD, FACS; Fallat, Mary E. MD, FACS; Gabbe, Belinda MAppSc, BPhysio, PhD, NHMRC CDF; Gfeller, Bob Jr. MBA; Gioia, Gerard PhD; Haut, Elliott MD, PhD; Hendrix, Jason; Hoeft, Chris; Hotz, Heidi RN; Keavany, Kathleen MHA; Levy-Carrick, Nomi MD, MPHIL; Manley, Geoffrey T. MD, PhD; Michetti, Christopher MD, FACS; Miller, Anna MD, FACS; Miller, Cate PhD; Morris, David S. MD, FACS; Naik-Mathuria, Bindi J. MD, MPH; Neal, Melanie; Patel, Bhavin; Newgard, Craig MD, MPH; Nitzschke, Stephanie MD, FACS; Okonkwo, David O. MD, PhD; Polk, Travis MD, FACS; Price, Michelle PhD, Med; Rivara, Fred MD, MPH; Sochor, Mark MD, MS, FACEP; Stein, Deb MD, MPH, FACS, FCCM; Subacius, Haris; Taylor, Gerry H. PhD; Thomas, William III; Wagner, Amy MD; Winfield, Rob MD, FACS; Zatzick, Douglas F. MD; Zielinski, Martin D. MD, FACS The Patient Reported Outcome Consortium Author Information
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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 | 0.161 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".