Is Emergency Medicine Overusing Valuable Resources?
Bibliographic record
Abstract
Over the past three decades, the growth of medical imaging has unquestionably helped patients by allowing physicians to diagnose disease and improve patients’ quality of life. However, overutilization of medical technologies, such as computed tomography (CT), can lead to an increase in the total public risk of cancer deaths due to radiation exposure. Although CT is an important technological advancement in medicine, approximately a third of all CT scans are performed needlessly. Children have a longer lifetime after radiation exposure and a greater radio-sensitivity than adults, hence they are at a higher risk of developing cancer. Extra caution should be taken when imaging children in the ED. Choosing Wisely, Image Gently, and ALARA are organizations that offer valuable tools to educate physicians about avoiding unnecessary medical treatments and reducing radiation exposure in patients. However, most ED physicians are aware of the problem of overimaging; lack of insight was not the reason why physicians were overimaging. The main reason was fear of malpractice. Solutions thought to be helpful for reducing unnecessary ED imaging included malpractice reform, physician feedback on test-ordering metrics, improved education of diagnostic imaging for physicians, educating patients to increase patient involvement, and shared decision making. These solutions could alleviate some of the ED physicians fears of malpractice and reduce the overuse of medical imaging technologies which can not only lessens patients’ radiation exposure but reduce the cost to the healthcare system.
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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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 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".