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 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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".