Research On Research : How Many Resident Presentations At Canadian Medical Imaging Research Days Go On To Publication?
Bibliographic record
Abstract
OBJECTIVE: Determine the percentage of resident presentations at Canadian medical imaging resident research days that go on to publication.METHODS: Each of the thirteen English speaking diagnostic imaging residency programs across Canada was contacted via email to request data and participation in the study. Programs were then asked to provide details about presentations at resident research days between 2013 and 2017, including presenter name, presentation title and abstract (if available). Internet searching was then preformed to confirm if presenters were medical imaging residents at the time. Repeat presentations on the same topic in subsequent years were excluded. In summer 2018, publications were identified via internet searching using resident name and keywords for each presentation via PubMed, web of science, and google. Identified publications were linked with the presentation to determine total number of publications and how many presentations resulted in publications by year at each school. Additional factors assessed were resident author order on publications, publishing journals, and time between research day presentation and publication.RESULTS: Data was obtained from 7 residency programs, with information from a total of 32 research days available to review. From this, 287 unique resident presentations were evaluated. 99/287 (34%) presentations generated a total of 118 publications. Publication rates were lower for 2017 than other years, and overall school publication rates ranged between 19% and 73% by school. 82/99 (82%) presentations generated a single publication with 15/99 (15%) generating multiple publications. Mean time from presentation to publication was 12.3 u00b1 13.6 months, with 48% of publications within 1 year following research day, and 14% published before research. 76/118 (64%) of publications listed the presenting resident as first author. The most common journal to publish in was the Canadian Association of Radiologists Journal with 16/118 (15%) publications. CONCLUSION: 34% of medical imaging research day presentations went on to publication, with variation in number of presentations and publications between schools.
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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 | 0.034 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".