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Record W2513974041 · doi:10.1097/pec.0000000000000831

Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting

2016· article· en· W2513974041 on OpenAlexaffabout
Ryan Halickman, Dennis Scolnik, Ayelet Rimon, Miguel Glatstein

Bibliographic record

VenuePediatric Emergency Care · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineNova scotiaPeer reviewPresentation (obstetrics)Library scienceMEDLINEFamily medicinePolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

Scientific presentations at professional organization meetings have long been recognized as a method of providing up-to-date and novel information to both the medical and scientific community. After abstract presentation at a medical conference, the subsequent publication rate of full-text articles is variable, and few studies have examined this topic with respect to international emergency medicine conferences. This study's goals were to determine the publication rate of articles resulting from abstracts presented at the 12th International Conference on Emergency Medicine 2008 in San Francisco, Calif, and to compare this with data from the previous International Conference on Emergency Medicine 2006 conference in Halifax, Nova Scotia, Canada. We found a reduction in publication rate from 33.2% in 2006 to 22.8% in 2008 and that the host country furnished a greater proportion of the abstracts. It would be interesting to examine how these potential trends played out over more extended periods.

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)0.143
metaresearch head score (Gemma)0.467
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.756

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.467
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0380.035
Science and technology studies0.0030.002
Scholarly communication0.0120.006
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0210.006

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 head0.515
GPT teacher head0.513
Teacher spread0.002 · 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
DomainReporting
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

Citations6
Published2016
Admission routes2
Has abstractyes

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