Publication rates in English of abstracts presented at the annual meeting of the Japanese Society of Psychiatry and Neurology
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".