Bibliographic record
Abstract
The demand for medical imaging in Canada has risen due to factors, including an aging population, increased patient volumes, advancements in procedures and treatments, and shifts in standard of care. Notably, there has been a substantial increase in CT and MRI examinations. Radiologists in Canada often have large patient volumes, work extended overtime, and manage prolonged wait-lists, all of which contribute to burnout. This burnout has an impact on productivity and staff turnover and may jeopardize patient safety. Burnout may also lead to radiologists reducing their work hours, seeking new employers, or leaving clinical practice. ChatGPT could play a role in supporting radiologists in a variety of ways, including generating radiology reports, providing structured report templates, assisting with clinical history sections of radiology reports, and facilitating patient communication. ChatGPT may also aid clinical decision support by assisting in final diagnoses and cancer screening decisions, and optimizing clinical decision support. ChatGPT’s limitations in radiology workflow include dependence on training data, potential inaccuracies in responses, ethical concerns about patient data privacy, and difficulties in handling complex radiology tasks. While ChatGPT holds promise in enhancing radiology workflow and patient care, careful consideration is needed for its limitations and potential risks. Responsible implementation and ongoing research and development are vital to leveraging its benefits while upholding patient safety and ethical standards.
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 | 0.036 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".