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Podium versus Poster Publication Rates at the Orthopaedic Trauma Association

2005· article· en· W2034544395 on OpenAlexaff
Charles Preston, Mohit Bhandari, Eric Fulkerson, Danial Ginat, Kenneth J. Koval, Kenneth A. Egol

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOrthopedic surgeryPresentation (obstetrics)Orthopedic traumaSports medicineGeneral surgerySurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Original studies at orthopaedic meetings are presented on the podium and in poster format. Publication of those studies in peer-reviewed journals is the standard of communicating scientific data to colleagues. Investigators of previous studies have reported publication rates, but never differentiated between the modes of presentation. We evaluated the annual meeting of the Orthopaedic Trauma Association from 1994-1998 and found that studies presented on the podium were 1.3 times more likely to be published than those presented in a poster format (67% versus 52%). The mean time to publication was similar, 21.6 months for poster presentations and 24.8 months for podium presentations. Podium presentations were more likely to be published in the Journal of Orthopaedic Trauma, Clinical Orthopaedics and Related Research, and the Journal of Bone and Joint Surgery (American and British editions). Our findings suggest different rates and distribution of publication between podium and poster presentations at an international trauma meeting. These findings should be considered when evaluating studies of interest at the Orthopaedic Trauma Association meeting.

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)0.058
metaresearch head score (Gemma)0.226
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.226
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.008
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0460.009

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 head0.152
GPT teacher head0.409
Teacher spread0.257 · 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.

Study designObservational
DomainEvaluation
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

Citations37
Published2005
Admission routes1
Has abstractyes

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