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Record W4220901432 · doi:10.1111/pcn.13351

Publication rates in English of abstracts presented at the annual meeting of the Japanese Society of Psychiatry and Neurology

2022· article· en· W4220901432 on OpenAlexaff
Kazunari Yoshida, Sho Moriguchi, Masahide Koda, Takuya Oka, Fumihiko Ueno, Saeko Ikai, Hideaki Tani, Masaru Mimura

Bibliographic record

VenuePsychiatry and Clinical Neurosciences · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineOdds ratioLogistic regressionImpact factorFamily medicinePeer reviewMEDLINEPsychiatryLibrary sciencePsychologyPolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Relatively low publication rates of abstracts presented at scientific meetings (i.e., 37.3%, 95% CI: 35.3-39.3) have been reported across various fields worldwide. However, no study has investigated the publication rate of abstracts presented at psychiatric meetings and factors associated with full publication in Japan. This study aimed to determine the proportion of conference abstracts in the psychiatric field that reach full publication in English and its associated factors in Japan. METHODS: A retrospective study was conducted to determine the publication rate of abstracts presented at the annual meetings of the Japanese Society of Psychiatry and Neurology (JSPN) in 2013 and 2014, the largest psychiatric meeting in Japan, by searching for full-text publications in PubMed and Google Scholar. Furthermore, we examined factors associated with a successful full publication of the conference abstract. RESULTS: Of the 737 abstracts evaluated, 132 (17.9%) were published in peer-reviewed journals; the publication rates for oral and poster presentations were 12.7% (46/363) and 23.0% (86/374), respectively. In multivariate logistic regression analyses, the following factors were significantly associated with successful publications: poster presentations (odds ratio [OR]: 1.67, 95% CI: 1.10-2.57), original studies (OR: 4.16, 95% CI: 2.44-7.47), and academic institutions (OR: 5.77, 95% CI: 3.44-10.19). CONCLUSIONS: The publication rate in English of the conference abstracts presented at the JSPN annual meetings was relatively lower than those in previous studies. Further encouragement of the publication of the abstracts presented in psychiatric conferences in Japan would be helpful in disseminating scientific findings in the field of psychiatry. This article is protected by copyright. All rights reserved.

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)0.052
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0520.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.299
GPT teacher head0.472
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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