Cost-Effectiveness of Diagnostic Lobectomy Versus Observation for Thyroid Nodules >4 cm
Bibliographic record
Abstract
BACKGROUND: The management of thyroid nodules >4 cm with benign cytology after fine-needle aspiration biopsy (FNAB) is controversial. FNAB is associated with a high false-negative rate in this setting, and may result in a delayed diagnosis and management of thyroid cancer. However, the majority of these nodules are benign. Therefore, the objective of this study was to determine the cost-utility of observation versus surgical management for thyroid nodules >4 cm with benign cytology after FNAB. METHODS: A microsimulation model comparing routine thyroid lobectomy with observation for low-risk patients with >4 cm thyroid nodules with benign FNAB cytology was constructed. Costs, quality-adjusted life-years (QALYs), and life-years gained were calculated over a lifetime time horizon from a U.S. Medicare perspective. RESULTS: The proportion of patients undergoing thyroid lobectomy for benign final pathology was 40% in the observation strategy versus 66% in the surgical strategy (p < 0.001). Overall, the surgical strategy was associated with higher lifetime costs compared with the observation strategy (incremental difference: + US$12,992 [confidence interval (CI) 13,042-13,524]), but also more QALYs (+0.12 QALYs [CI 0.02-0.24]) and longer life expectancy (+1.67 years [CI 1.00-2.41]). Incremental lifetime costs were lower for patients <55 years compared with those ≥55 years (+11,181 vs. +14,811, p < 0.001). The probability of cost-effectiveness of the surgical strategy was 49% at a $100k/QALY threshold or 65% at a $100k/life-year gained threshold. CONCLUSIONS: Routine thyroid lobectomy is associated with improved outcomes at an acceptable cost compared with observation for thyroid nodules >4 cm with benign cytology after FNAB. Surgical resection may be a cost-effective strategy to rule out malignancy in these nodules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".