Publication Rates of Abstracts Presented at the 2007 and 2010 Canadian Association of Radiation Oncology Meetings
Bibliographic record
Abstract
BACKGROUND: We set out to determine the rate, time-trend, and defining factors associated with publication of abstracts presented at two annual scientific meetings of the Canadian Association of Radiation Oncology (caro). METHODS: All abstracts accepted for oral presentation in 2007 and 2010 were obtained from the caro program archives and searched using the PubMed database. Variables in the dataset included the year of presentation at caro and of publication in a scientific journal, time to publication (in months), publishing journal, impact factor of publishing journal, abstract research type (clinical, technical, or basic science) and disease site, country of origin, and university of the first author. RESULTS: Overall, 88 of 172 abstracts from the 2007 (n = 102) and 2010 (n = 70) caro meetings were published in peer-reviewed journals (publication rate: 51.2%). Mean time to publication was 18.5 months. Among research types, clinical research (62.5%) and, among disease sites, prostate cancer (40.4%) were most likely to be published. Of all the abstracts, 50.1% were contributed by only 2 universities, a proportion that resembles the overall abstract publication rate of 51.2%. The conversion rate for those 2 universities (51.1%) is very similar to that for all abstracts presented at the two meetings. CONCLUSIONS: Half the abstracts presented at the 2007 and 2010 caro meetings were ultimately published in journals indexed in PubMed by about 1.5 years after presentation. Half the abstracts and publications came from just 2 universities; more must to be done to close the gap.
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 | 0.018 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| 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".