Canadian Association of Radiologists Annual Scientific Meetings: How Many Abstracts Go on to Publication?
Bibliographic record
Abstract
PURPOSE: To determine the percentage of abstracts presented at the Canadian Association of Radiologists (CAR) annual scientific meetings that go on to publication. METHODS: Records of previous CAR meetings from the years 2005-2011 were obtained. An Internet search was performed to determine which abstracts went on to publication. Abstracts were assessed according to exhibit category (Resident Award Papers), educational institution, publishing journal, and time to publication. RESULTS: Of the 402 abstracts presented, 112 (28%) were published. Overall, an average of 37% of Radiologists-In-Training Presentations, 34% of Scientific Exhibits, and 20% of Educational Exhibits went on to publication. The University of British Columbia and University of Ottawa published the largest number of abstracts (66 and 62, respectively) from the years 2005-2011. The University of Montreal had the largest percentage of abstracts published (42%). The range of publishing journals was wide, but the top publisher was the Canadian Association of Radiologists Journal (27%). Eighty-three percent of abstracts were published within 3 years of being presented. CONCLUSION: In total, 28% of all the abstracts presented at the CAR conferences between 2005 and 2011 were published. Further exploration into the reasons and barriers for abstracts not being published may be a next step in future research.
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 | 0.024 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.024 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".