Comparison of single photon emission CT (SPECT) with SPECT/CT imaging in preoperative localization of parathyroid adenomas: A cost‐effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Controversy exists regarding the superiority of single photon emission CT (SPECT)/CT over SPECT for preoperative localization of parathyroid adenomas in primary hyperparathyroidism (PHPT), as well as the cost-effectiveness. METHODS: A retrospective review was undertaken of patients undergoing surgery for PHPT from January 2009 to August 2014. Ultrasound and SPECT (ultrasound-SPECT) or SPECT/CT (ultrasound-SPECT/CT) were performed for each patient. Sensitivity and positive predictive value (PPV) of each modality were calculated. Cost-effectiveness was determined by an incremental cost-effectiveness ratio (ICER) analysis. RESULTS: Two hundred fifty-nine patients with 266 parathyroid adenomas were included in the study. Lateralization sensitivity and PPV of ultrasound-SPECT were 85.1% and 98.2%, respectively. The lateralization sensitivity and PPV of ultrasound-SPECT/CT were 86.9% and 99.4%, respectively. A cost of $2499.22 (CAD) per additional parathyroid adenoma detection by ultrasound-SPECT/CT was determined from the ICER analysis. CONCLUSION: Similar sensitivities and PPVs were observed between ultrasound-SPECT and ultrasound-SPECT/CT in preoperatively lateralizing parathyroid adenomas, with relatively equivalent cost-effectiveness. © 2016 Wiley Periodicals, Inc. Head Neck 38: E2062-E2065, 2016.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".