Pediatric thyroid FNA biopsy: Outcomes and impact on management over 24 years at a tertiary care center
Bibliographic record
Abstract
BACKGROUND: Thyroid malignancy is rare in young children, although the incidence increases sharply during adolescence. Nodular thyroid disease and thyroid cancer in children differ substantially from those in adults, because the rates of malignancy among children are roughly 5-fold higher, and local and distant metastases as well as recurrences are more common. Even with the recent introduction of pediatric guidelines, there remains a paucity of pediatric data on which to base clinical decision making. The objectives of this study were to characterize the outcomes of fine-needle aspiration biopsy (FNAB) of nodular thyroid disease at a pediatric tertiary-care institution over a 24-year period and to relate cytopathology to histopathology and management decisions in this population. METHODS: A retrospective review of patients who underwent preoperative FNAB and thyroid surgery between 1992 and 2015 was conducted. In total, 207 nodules were biopsied among 178 patients. RESULTS: Adequate FNAB samples were obtained in 74% of biopsies. Sixty-five patients underwent thyroidectomy after FNAB. In this group, the malignancy rates for lesions deemed benign, atypical, suspicious, and malignant on FNAB cytology were 16%, 67%, 71%, and 100%, respectively. Twenty-seven individuals underwent >1 biopsy; however, no malignancies were identified in these patients. Surprisingly, the rate of malignancy in patients who underwent preoperative FNAB was not significantly different from the rate in those who proceeded directly to surgery (n = 146). CONCLUSIONS: FNAB remains a valuable tool for preoperative assessment of pediatric thyroid nodules. When samples are adequate for assessment, cytology other than clearly "benign" merits referral for diagnostic or therapeutic thyroidectomy. In this series, FNAB did not reduce rates of surgery for benign disease. Cancer Cytopathol 2016;124:801-10. © 2016 American Cancer Society.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".