Pathological Features Frequently Found in Recurrent Papillary Thyroid Cancer
Bibliographic record
Abstract
Objectives: Despite the rising prevalence of malignant papillary thyroid carcinoma, locoregional recurrence remains low. Clinico‐pathological features associated with recurrence are not well defined. The objective of this study is to describe and evaluate the various pathological features found in patients with recurrent papillary thyroid carcinoma. Methods: A retrospective review was conducted identifying patients who were found to have recurrent papillary thyroid carcinoma between July 2006 and May 2013 at the McGill University Thyroid Cancer Centre. Results: There was a total of 552 patients with malignant papillary thyroid carcinoma. Over the study period, recurrent disease occurred in 2.0% of patients (n = 11), of whom 10 were pT3N1b and 1 was a pT4aN1. When these patients were compared with the 541 patients who did not have recurrence of their disease, there were significant differences for sex ( P =. 0271), size of primary tumor ( P =. 0355), positive margins (=0 0002), lymphovascular invasion ( P <. 0001), extrathyroidal extension ( P =. 0001), lymph node metastasis ( P <. 0001), and extra‐nodal extension ( P <. 0001). There was no significant difference for age, presence of multifocal disease, and perineural invasion. On regression analysis, positive margins, lymphovascular invasion, regional lymph node metastasis, and extra‐nodal extension were found to be independent predictors of recurrence ( P =. 0011, P =. 0248, P =. 0146, and P =. 0007, respectively). Conclusions: In patients with papillary thyroid cancer, sex, tumor size, and extra‐thyroidal extension are features frequently found in patients with recurrence. However, positive surgical margins, lymphovascular invasion, lymph node metastasis, and extra‐nodal extension were found to be independent predictors of recurrence in this study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".