MétaCan
Menu
Back to cohort
Record W2433999710

Thirty years of the "Anica Bitenc Travelling Fellowship" - a tribute to Dr. Igor A.S. Bitenc.

2015· article· en· W2433999710 on OpenAlexaboutno aff
Z Mikić

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsEndowmentTributeNobilityHomelandMedicinePolitical scienceHistoryAncient historyClassicsLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Thirty years ago, in 1984, Dr. Igor A. S. Bitenc, M.D., F.R.C.S.(C), a retired orthopaedic surgeon from Canada, who was of Yugoslav descent, and who had always remained attached to his original homeland, founded a travelling fellowship for young orthopaedic surgeons from former Yugoslavia named the "Anica Bitenc Travelling Fellowship" after his late mother. The first Fellow visited Canada in 1985 and was followed by five Fellows from different areas of Yugoslavia in successive years. Due to the tragic war in Yugoslavia in the 90's, the Fellowship was postponed for several years. It was resumed in 1997, but only for three newly formed states of Croatia, Slovenia and Serbia. Funding of the Fellowship was provided by an endowment from Dr. Bitenc and managed by the Canadian Orthopaedic Association, which hosts one Fellow per year on the rotational basis from Slovenia, Croatia and Serbia. The Fellowship finally proved to be very successful and, owing to the benevolence of Dr. Bitenc, 24 young orthopaedic surgeons from various regions of former Yugoslavia have had the opportunity to visit the best orthopaedic centres in Canada so far. Dr. Bitenc has ensured the Fellowship will continue for many years to come by bequeathing an endowment of $ 300.000. This unforgettable act of human nobility and patriotic sensibility will be of enormous help in future to many orthopaedic surgeons and their numerous patients in the countries of South Slavs.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.256
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicMusculoskeletal Disorders and RehabilitationFrench-language works237,207