Cost-effective Management of Low-Risk Papillary Thyroid Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To compare the 20-year cost-effectiveness of initial hemithyroidectomy vs total thyroidectomy in the management of small papillary thyroid cancer in the low-risk patient. DESIGN: Pooled data from the published literature were used to determine key statistics for decision analysis such as rates of recurrence, rates of complications for all interventions undertaken, and rates of death. The 2005 costs were obtained from the US Department of Health and Human Services, as well as from Medicare reimbursement schedules. Future costs were discounted at 6%. SETTING: Decision analysis study. PATIENTS: Data from the published literature. MAIN OUTCOME MEASURES: A state-transition (Markov) decision model was constructed based on the most recent American Thyroid Association recommendations. A cost-effectiveness analysis was performed using fixed probability estimates and Monte Carlo microsimulation, with effectiveness defined as cause-specific mortality or recurrence-free survival. After identifying initial results, sensitivity and threshold analyses were performed to assess the strength of the recommendations. RESULTS: Initial probability estimates were determined from a review of 940 abstracts and 31 relevant studies examining outcomes in patients with low-risk thyroid cancer undergoing thyroidectomy or neck dissection. During 20 years, cost estimates (including initial surgery, follow-up, and treatment of recurrence) were between $13,896.81 and $14,241.24 for total thyroidectomy and between $15,037.58 and $15,063.75 for hemithyroidectomy. Cause-specific mortality was similar for both treatment strategies, but recurrence-free survival was higher in the total thyroidectomy group. Sensitivity and threshold analyses demonstrated that these results were sensitive to rates of recurrence and cost of follow-up but remained robust when compared with willingness to pay. CONCLUSIONS: Total thyroidectomy dominates over hemithyroidectomy as initial treatment for low-risk papillary thyroid cancer. However, in sensitivity analyses, these results varied by institution because of heterogeneity in long-term treatment outcomes. With changing protocols of management, it is possible that hemithyroidectomy will emerge as being more cost-effective. Long-term prospective trials are necessary to validate our findings.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".