Incidence and Economic Impact of Incidental Findings on <sup>18</sup> F-FDG PET/CT Imaging
Bibliographic record
Abstract
PURPOSE: The study sought to determine the incidence of incidental findings on whole-body positron emission tomography with computed tomography (PET/CT) imaging and the average costs of investigations to follow-up or further characterize incidental findings. METHODS: Imaging reports of 215 patients who underwent whole-body PET/CT imaging were retrospectively reviewed. Our provincial picture archiving and communication system was queried and patient charts were reviewed to identify all investigations performed to follow-up incidental findings within 1 year of the initial PET/CT study. Costs of follow-up imaging studies (professional and technical components) and other diagnostic tests and procedures were determined in Canadian dollars (CAD) and U.S. dollars (USD) using the 2015 Ontario Health Insurance Plan Schedule of Benefits and Fees and 2016 U.S. Medicare Physician Fee Schedule, respectively. RESULTS: At least 1 incidental finding was reported in 161 reports (74.9%). The mean number of incidental findings ranged from 0.64 in patients <45 years of age to 2.2 in patients 75 years of age and older. Seventy-five recommendations for additional investigations were made for 64 (30%) patients undergoing PET/CT imaging, and 14 of those were carried out specifically to follow-up incidental findings. Averaged across all 215 patients, the total cost of investigations recommended to follow-up incidental findings was CAD$105.51 (USD$127.56) per PET/CT study if all recommendations were acted on, and CAD$22.77 (USD$29.14) based on investigations actually performed. CONCLUSIONS: As the incidence of incidental findings increases with age and a larger proportion of elderly patients is expected as population demographics change, it will be increasingly important to consider incidental findings on PET/CT imaging with standardized approaches to follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".