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Record W4247654680 · doi:10.1097/brs.0b013e31826d62ed

Terminology

2012· article· en· W4247654680 on OpenAlexaffabout
Paul A. Anderson, Gunnar Andersson, Paul M. Arnold, Darrel S. Brodke, Erika Brodt, Jens R. Chapman, Dean Chou, Mark B. Dekutoski, Joseph R. Dettori, John G. DeVine, Claire G. Ely, Michael G. Fehlings, Dena J. Fischer, Daryl R. Fourney, Mitchell A. Hansen, Christopher C. Harrod, Robin Hashimoto, Jeffrey T. Hermsmeyer, Alan S. Hilibrand, Manish K. Kasliwal, Michael P. Kelly, Han Jo Kim, Paul Kraemer, Brandon D. Lawrence, Michael J. Lee, Lawrence G. Lenke, Daniel C. Norvell, Annie Raich, K. Daniel Riew, Christopher I. Shaffrey, Andrea C. Skelly, Justin S. Smith, Christopher J. Standaert, Ellen M. Van Alstyne, Jeffrey C. Wang

Bibliographic record

VenueSpine · 2012
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsUniversity of SaskatchewanRoyal University HospitalUniversity of Toronto
Fundersnot available
KeywordsTerminologyMedicineGerontologyLinguistics

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.088
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.013
Science and technology studies0.0040.005
Scholarly communication0.0120.013
Open science0.0060.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0880.081

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 head0.026
GPT teacher head0.394
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations17
Published2012
Admission routes2
Has abstractyes

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