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Record W2089174192 · doi:10.2106/jbjs.n.01209

2014 Austrian-Swiss-German Fellowship

2015· review· en· W2089174192 on OpenAlexaboutno aff
James Slover, Phillip Heaton, Ahmad Nassr

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2015
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsGermanPilgrimKingdomMedicineWest germanyLibrary scienceFamily medicineDemographyGeographyHistoryEconomic historySociologyArchaeology

Abstract

fetched live from OpenAlex

The Austrian-Swiss-German (ASG) Traveling Fellowship, started in 1979, is a unique opportunity for surgeons from the United States, Canada, and the United Kingdom to travel for an extended period to premier orthopaedic surgery sites throughout Austria, Switzerland, and Germany every other year. In turn, the United States, Canada, and the United Kingdom host representatives from Austria, Switzerland, and Germany in the intervening years. In 2014, three of us were chosen to participate: Dr. Ahmad Nassr from the Mayo Clinic in Rochester, Minnesota, and Dr. James Slover from NYU Langone Medical Center in New York, NY, both sponsored by the American Orthopaedic Association; and Dr. Phillip Heaton from Pilgrim Hospital in Boston, England, sponsored by the British Orthopaedic Association.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.158
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1580.062

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 head0.101
GPT teacher head0.373
Teacher spread0.272 · 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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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