Clinical Safety of Renaming Encapsulated Follicular Variant of Papillary Thyroid Carcinoma: Is NIFTP Truly Benign?
Bibliographic record
Abstract
BACKGROUND: Renaming encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC) to noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was recently suggested to prevent the overtreatment, cost and stigma associated with this low-risk entity. The purpose of this study is to document the incidence and further assess the clinical outcomes of reclassifying EFVPTC to NIFTP. METHODS: We searched synoptic pathologic reports from a high-volume academic endocrine surgery hospital from 2004 to 2013. The standard of surgical pathology practice was based on complete submission of malignant thyroid nodules along with the nontumorous thyroid parenchyma. Rigid morphological criteria were used for the diagnosis of noninvasive EFVPTC, currently known as NIFTP. A retrospective chart review was conducted looking for evidence of malignant behavior. RESULTS: One hundred and two patients met the strict inclusion criteria of NIFTP. The incidence of NIFTP in our cohort was 2.1% of papillary thyroid cancer cases during the studied time period. Mean follow-up was 5.7 years (range 0-11). Five patients were identified with nodal metastasis and one patient with distant metastasis. Overall, six patients showed evidence of malignant behavior representing 6% of patients with NIFTP. CONCLUSION: Our study demonstrates that the incidence of NIFTP is significantly lower than previously thought. Furthermore, evidence of malignant behavior was seen in a significant number of NIFTP patients. Although the authors fully support the de-escalation of aggressive treatment for low-risk thyroid cancers, NIFTP behaves as a low-risk thyroid cancer rather than a benign entity and ongoing surveillance is warranted.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".