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Body mass index affects outcomes after vertebral body tethering surgery
Stefan Parent, Firoz Miyanji, Kevin Smit, Joshua Murphy, Riley Bowker, Nedal Al Khatib, Ron El‐Hawary, Abdullah Abdullah, Edward S. Ahn, Behrooz A. Akbarnia, Harry Akoto, Stephen A. Albanese, Jason Anari, John T. Anderson, Richard C. E. Anderson, Lindsay M. Andras, Jennifer M. Bauer, Laura L. Bellaire, Randy Betz, Craig M. Birch, Laurel C. Blakemore, Oheneba Boachie-Adjei, Chris Bonfield, Daniel Bouton, Félix Brassard, Douglas L. Brockmeyer, Jaysson T. Brooks, David B. Bumpass, Pat Cahill, Olivier Chémaly, Jason Pui Yin Cheung, Kmc Cheung, Robert Cho, Tyler Christman, Eduardo C. Beauchamp, Daniel E. Couture, Haemish Crawford, Alvin H. Crawford, Benny Dahl, Gökhan Demirkıran, Dennis P. Devito, Mohammad Diab, Hazem El Sebaie, John B. Emans, Mark Erickson, Jorge Fabregas, Frances A. Farley, David M. Farrington, Graham T. Fedorak, Ryan Fitzgerald, Nicholas D. Fletcher, Lorena V. Floccari, Jack Flynn, Peter G. Gabos, Adrian Gardner, Sumeet Garg, FRANK J. GEROW, Michael Glotzbecker, Jaime A. Gómez, David Gonda, Tenner J. Guillaume, Purnendu Gupta, Kyle G. Halvorson, Kim Hammerberg, Christina K. Hardesty, Daniel Hedequist, Michael J. Heffernan, John Heflin, Ilkka Helenius, Jose Herrera, Grant D. Hogue, Josh Holt, Jason Howard, M. Timothy Hresko, Steven W. Hwang, Stephanie Ihnow, Brice Ilharreborde, Kenneth D. Illingworth, Viral V. Jain, Andrew Jea, Megan Johnson, Charles E. Johnston, Morgan Jones, Judson Karlen, Lawrence I. Karlin, Danielle Katz, Noriaki Kawakami, Brian P. Kelly, Derek M. Kelly, Raymond Knapp, Paul Aarne Koljonen, Kenny Kwan, Hubert Labelle, Robert K. Lark, A. Noelle Larson, William F. Lavelle, Lawrence G. Lenke, Sean M. Lew, Gertrude Li, Craig R. Louer, Scott J. Luhmann, Jean‐Marc Mac‐Thiong, Stuart A. Mackenzie, Erin MacKintosh, Francesco T. Mangano, David Marks, Sánchez Márquez, Jonathan E. Martin, Jeffrey E. Martus, Antònia Matamalas, Oscar H. Mayer, Richard E. McCarthy, Amy L. McIntosh, Jessica McQuerry, Jwalant Mehta, Lionel N. Metz, Daniel Miller, Greg Mundis, Josh Murphy, Robert F. Murphy, Karen S. Myung, Susan E. Nelson, Peter O. Newton, Matthew Newton Ede, Cynthia Nguyen, Matthew E. Oetgen, Timothy Oswald, Jean Ouellet, Josh Pahys, Kathryn Palomino, Alejandro Peiró‐García, Ferrán Pellisé, Joseph H. Perra, Jonathan J. Phillips, Javier Pizones, Selina Poon, Nigel Price, Norman Ramírez-Lluch, Brandon Ramo, Gregory J. Redding, Todd F. Ritzman, Luis F. Rodriguez, Juan Carlos Rodriguez-Olaverri, David P. Roye, Benjamin D. Roye, Lisa Saiman, Amer F. Samdani, Francisco Sánchez Pérez-Grueso, James O. Sanders, Jeffrey R. Sawyer, Christina M. Sayama, Michael L. Schmitz, Jacob F. Schulz, Richard M. Schwend, Suken A. Shah, Jay R. Shapiro, Harry L. Shufflebarger, David L. Skaggs, John T. Smith, Brian D. Snyder, Paul D. Sponseller, George Stephen, Joe A. Stone, Peter Sturm, Hamdi Sukkarieh, Ishaan Swarup, Michal Szczodry, John Thometz, George H. Thompson, Walter H. Truong, Raphaël Vialle, Michael G. Vitale, John S. Vorhies, Eric J. Wall, Shengru Wang, Bill Warner, Stuart L. Weinstein, Michelle C. Welborn, Klane K. White, David Wrubel, Nan Wu, Kwadwo Poku Yankey, Burt Yaszay, Muharrem Yazıcı, Jianguo Zhang
Bibliographic record
VenueSpine Deformity · 2022
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsChildren's Hospital of Eastern OntarioBC Children's HospitalCentre Hospitalier Universitaire Sainte-JustineIzaak Walton Killam Health Centre
Fundersnot available
KeywordsOverweightUnderweightMedicineScoliosisBody mass indexKyphosisOrthopedic surgeryPost-hoc analysisSurgeryAnalysis of varianceInternal medicineRadiography
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 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)0.000
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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 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
Citations7
Published2022
Admission routes1
Has abstractno