Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Terminology—Adjacent Segment Pathology We the undersigned propose “Adjacent Segment Pathology” as the general term to describe changes that occur adjacent to a previously operated level. Under this heading, “Radiographic Adjacent Segment Pathology” (RASP) refers to radiological changes that occur at the adjacent segment. “Clinical Adjacent Segment Pathology” (CASP) refers to clinical symptoms and signs that occur at the adjacent segment. The purpose of this new nomenclature is to: Standardize terminology for clinicians so that they are speaking the same regarding definitions. Set the stage for more meaningful and logical classification of disease. Assist in separating out conditions that may require intervention from those that may not require intervention. Provide a more logical description of the primary considerations—radiographical, which may/may not correlate with symptoms and need additional intervention versus clinical, which clarifies that patient symptomatology is present. Simplify future literature searches and research on the topic. We can best accomplish this by eliminating the plethora of terms that have been utilized to describe the various pathologies that occur at the adjacent level. FigurePaul A. Anderson, MD University of Wisconsin Gunnar B. J. Andersson, MD, PhD Midwest Orthopaedics at Rush University Paul M. Arnold, MD, FACS University of Kansas Darrel S. Brodke, MD University of Utah Erika D. Brodt, BS Spectrum Research, Inc. Jens R. Chapman, MD University of Washington Dean Chou, MD University of California, San Francisco Mark Dekutoski, MD The Mayo Clinic Joseph R. Dettori, MPH, PhD Spectrum Research, Inc. John G. DeVine, MD Dwight D. Eisenhower Army Medical Center Claire G. Ely, BS Spectrum Research, Inc. Michael G. Fehlings, MD, PhD, FRCSC University of Toronto Dena J. Fischer, DDS, MSD, MS Spectrum Research, Inc. Daryl R. Fourney, MD, FRCSC, FACS University of Saskatchewan, Royal University Hospital Mitchell A. Hansen, BS, MBBS, Grad Dip Sc, PhD, FRACS University of Toronto Christopher Chambliss Harrod, MD Thomas Jefferson University, Rothman Institute Robin Hashimoto, PhD Spectrum Research, Inc. Jeffrey T. Hermsmeyer, BS Spectrum Research, Inc. Alan S. Hilibrand, MD Thomas Jefferson University, Rothman Institute Manish K. Kasliwal, MD, MCh University of Virginia Michael P. Kelly, MD Washington University Han Jo Kim, MD Washington University Paul Kraemer, MD Indiana Spine Group Brandon D. Lawrence, MD University of Utah Michael J. Lee, MD University of Washington Lawrence G. Lenke, MD Washington University Daniel C. Norvell, PhD Spectrum Research, Inc. Annie Raich, MPH Spectrum Research, Inc. K. Daniel Riew, MD Washington University Christopher I. Shaffrey, MD, FACS University of Virginia Andrea C. Skelly, MPH, PhD Spectrum Research, Inc. Justin S. Smith, MD, PhD University of Virginia Christopher J. Standaert, MD University of Washington Ellen M. Van Alstyne, MS Spectrum Research, Inc. Jeffrey C. Wang, MD University of California, Los Angeles
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.
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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it