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Record W4411128736 · doi:10.1177/21925682251343521

The AO Spine Knowledge Forums: A Decade of Impactful Spine Research

2025· editorial· en· W4411128736 on OpenAlexaff
Klaus John Schnake, Michael G. Fehlings, Niccole Germscheid, Shekar N. Kurpad, Ilya Laufer, Stephen J. Lewis, Gregory D. Schroeder, S. Tim Yoon

Bibliographic record

VenueGlobal Spine Journal · 2025
Typeeditorial
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineSPINE (molecular biology)Spinal cord injuryKnowledge translationPhysical therapySpinal cordKnowledge managementBioinformaticsComputer science

Abstract

fetched live from OpenAlex

The AO Spine Knowledge Forums are independent expert-driven global study groups dedicated to improving patient care by publishing evidence-based recommendations and conducting high-impact clinical studies. Five Knowledge Forums represent 6 spine pathologies: tumor, deformity, spinal cord injury, degeneration, trauma, and infection. A summary highlighting their most impactful research achievements over the past 10 years is provided. The results illustrate the critical clinical role of these independent Knowledge Forums.

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)0.030
metaresearch head score (Gemma)0.098
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.098
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.003
Science and technology studies0.0040.004
Scholarly communication0.0160.011
Open science0.0030.004
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0100.006

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.

Opus teacher head0.025
GPT teacher head0.440
Teacher spread0.415 · 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
GenreEditorial

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

Citations0
Published2025
Admission routes1
Has abstractyes

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