Lessons From the Tc-99m Shortage
Bibliographic record
Abstract
BACKGROUND: In 2009, the Chalk River nuclear reactor closed for repairs that led to a critical shortage of technetium-99m (Tc-99m). Several centers used thallium-201 (Tl-201) as an alternative radiotracer for myocardial perfusion imaging. Because Tl-201 is considered by many as a suboptimal radiotracer, we sought to understand the impact of using Tl-201 (during the Tc-99m shortage) on downstream resource utilization. METHODS AND RESULTS: We performed a retrospective study at the Ottawa Heart Institute of 7402 patients (60% men; mean age, 62.6 ± 11.8 years), patients were referred for myocardial perfusion imaging between May 2008 and January 2011 (PRE_Tc-99m [2938 patients]), during (DURING_Tl-201 [2959 patients]), and after (POST_Tc-99m [1505 patients]) the Tc-99m shortage. Patients were followed for 6 months after their index myocardial perfusion imaging to determine subsequent rates of cardiac catheterization or noninvasive imaging. More downstream testing was seen in the Tl-201 cohort (639 [21.4%] patients) than the Tc-99m cohort (537 [12.1%] patients; P<0.001). After adjustment using propensity scores, differences in downstream referral rates were maintained. The downstream investigations resulted in an estimated increase in per-patient costs ($165.22; 95% confidence interval, 153.00-177.42) in the DURING_Tl-201 cohort compared with the Tc-99m cohort ($90.97; 95% confidence interval, 83.42-98.90; P<0.001). As well, the mean effective radiation dose per-patient was higher in DURING_Tl-201 (23.57 mSv; 95% confidence interval, 23.16-23.96) than in Tc-99m (12.92 mSv; 95% confidence interval, 12.55-13.40; P<0.001). CONCLUSIONS: In this single-center study, the use of Tl-201 during the Tc-99m shortage was associated with an increase in downstream testing, cost, and patient radiation exposure, but these findings may not be generalizable to other centers. Although Tl-201 provided a short-term solution to the unexpected Tc-99m shortage, long-term cost-effective solutions should be areas of future study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".