Predictors of malignancy in preoperative nondiagnostic biopsies of the thyroid.
Bibliographic record
Abstract
OBJECTIVE: To determine whether preoperative variables can be used to predict malignancy for thyroid nodules with follicular, Hürthle, or nondiagnostic cytology on fine-needle aspiration biopsy (FNAB). MATERIALS AND METHODS: Retrospective analysis of 77 consecutive patients selected for total or subtotal thyroidectomy for follicular, Hürthle, or nondiagnostic lesions of the thyroid in two university hospitals. Eleven clinical variables, as well as nodule size, multiplicity, and ultrasound calcifications, were correlated with final histopathologic diagnosis of benign or malignant disease. Analysis was preformed using the Pearson chi-square test. RESULTS: The overall rate of malignancy in our series was 61% (n = 47). FNABs classified as follicular or Hürthle lesions without cellular atypia had a significantly lower risk of malignancy (49% vs 71%; p = .05). Patients who presented with a solitary nodule and FNAB cellular atypia displayed an increased risk of malignancy (92% vs 55%; p = .011). The rate of malignancy was higher for patients with a positive family history (100% vs 59%), a solitary nodule (73% vs 53%), cellular atypia (76% vs 54%), or intrathyroidal calcifications on ultrasonography (71% vs 57%), although none were found to be statistically significant (p > .05). Male gender, age > 45 years, nodule size > 3 cm, mass effect symptoms, and radiation exposure to the neck were not associated with malignancy in our series. CONCLUSION: When presented with follicular, Hürthle, or nondiagnostic biopsies for thyroid nodules, thyroid surgeons should rely systematically on sonographic findings and cytopathologic features to guide their management approach.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".