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Record W2592587463 · doi:10.1080/17453674.2017.1297918

Aarhus Regenerative Orthopaedics Symposium (AROS)

2016· article· en· W2592587463 on OpenAlexaff
Casper Bindzus Foldager, Michael Bendtsen, Lise Charlotte Berg, Jan E. Brinchmann, Mats Brittberg, Cody Bünger, José A. Canseco, Li Chen, Bjørn Borsøe Christensen, Pauline Colombier, Bent Deleuran, James Edwards, Brian Elmengaard, Jack Farr, Birgitta Gatenholm, Andreas H. Gomoll, James Hoi Po Hui, Rune Bruhn Jakobsen, Natasja Leth Joergensen, Moustapha Kassem, Thomas Koch, Søren Kold, Michael R. Krogsgaard, Henrik Lauridsen, Dang Quang Svend Le, Catherine Le Visage, Martin Lind, Jens Vinge Nygaard, Morten Lykke Olesen, Michael Pedersen, Martin Wyman Rathcke, James B. Richardson, Sally Roberts, Daisuke Sakai, Wei Seong Toh, Jill Urban, Myron Spector

Bibliographic record

VenueActa Orthopaedica · 2016
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversity of Guelph
FundersMedical Research CouncilNovo Nordisk FondenNational Research FoundationNational Centre for the Replacement, Refinement and Reduction of Animals in ResearchVersus ArthritisDanmarks Grundforskningsfond
KeywordsMedicineOrthopedic surgerySurgery

Abstract

fetched live from OpenAlex

The combination of modern interventional and preventive medicine has led to an epidemic of ageing. While this phenomenon is a positive consequence of an improved lifestyle and achievements in a society, the longer life expectancy is often accompanied by decline in quality of life due to musculoskeletal pain and disability. The Aarhus Regenerative Orthopaedics Symposium (AROS) 2015 was motivated by the need to address regenerative challenges in an ageing population by engaging clinicians, basic scientists, and engineers. In this position paper, we review our contemporary understanding of societal, patient-related, and basic science-related challenges in order to provide a reasoned roadmap for the future to deal with this compelling and urgent healthcare problem.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.265
Teacher spread0.250 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

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