Incidence and Histopathological Behavior of Papillary Microcarcinomas: Study of 429 cases
Bibliographic record
Abstract
OBJECTIVE: We aim to present papillary microcarcinoma (PMC) incidence at a university teaching hospital, to compare characteristics of PMC in relation to size, and to assess for significant difference in PMC incidence among patients with non-PMC thyroid malignancies. MATERIALS AND METHODS: Pathology results were reviewed for consecutive total thyroidectomies between 2002 and 2007 (n = 860). Statistical significance was calculated using chi(2) or, when unavailable, Fisher exact test. RESULTS: PMC was found in 429 cases, which is 49.9 percent of all total thyroidectomies. In PMC > or =5 mm, 25.1 percent had extrathyroidal extension vs 9.1 percent for <5 mm (P < 0.001). When 4 mm is used as a threshold, P value was 300-fold smaller. Incidence in patients with any non-PMC thyroid malignancy was 51.6 percent against 47.2 percent in all other patients (P = 0.203). CONCLUSIONS: In this study, PMC was found in 49.9 percent of patients, which, to our knowledge, is higher than any other reported incidence. A threshold of > or =4 mm was more significant than 5 mm for carrying increased risk for extrathyroidal spread. There was no significant difference in PMC incidence in patients with malignant vs benign disease.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".