A Comparative Evaluation of Cardiothoracic Radiology Fellowship Website Content
Bibliographic record
Abstract
PURPOSE: Prospective radiology fellows often rely on the internet to obtain information with regard to the application process for and the unique qualities of different fellowship programs. The aim of this study was to analyze the content of websites of the United States' and Canadian cardiothoracic radiology fellowships. METHODS: All active Cardiothoracic Radiology fellowship websites as of July 2019 were evaluated and compared using 25 criteria in the following domains: Application, Recruitment, Clinical Training, Education/Research, and Incentives. Program website information availability was compared by geographic region. RESULTS: There were 60 active cardiothoracic radiology fellowships, and 59 of these fellowships had a dedicated fellowship website. Websites, on average, had 9.3 of the 25 criteria (37.2%). The mean number of schools that satisfied the criterion in the "Incentives" domain ([7.75/59] 10.5%±2.8%) was significantly lower than that for the "Application Process" domain ([40.50/59]; 68.7%±40.6%) (P=0.01). There was no significant difference in the information content of programs in different geographic regions (P=0.246). CONCLUSION: Most cardiothoracic radiology fellowship websites were lacking content relevant to prospective fellows. Provision of more relevant and easily accessible online content may support programs to better inform and recruit residents and to promote the specialty of cardiothoracic radiology.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".