Hyalinizing trabecular tumour: review and new insights into the molecular biology.
Bibliographic record
Abstract
OBJECTIVE: To review the current literature on hyalinizing trabecular tumor (HTT) and the relationship to other, more sinister neoplasms of the thyroid including papillary thyroid carcinoma (PTC). To apply this information to clinical practice and thus elucidate the best approach regarding the management of patients with thyroid tumours. METHODS: A thorough literature search was performed on articles published on HTT thus far. Articles were then reviewed for epidemiology, discussion of categorization, and possible areas of differentiation from other thyroid tumor types. Our own hospital records were all reviewed for any possible cases of HTT. RESULTS: There have been many attempts made to find objective ways of categorizing HTT from other thyroid tumors. The articles found highlighted the difficulties in understanding the classification, molecular genetics, and pathology of HTT, especially with regard to the differentiation from PTC. In our own institution, we found only one such case of confirmed HTT. CONCLUSIONS: The ability to differentiate between HTT and PTC remains imperfect. Currently, permanent histologic sections are the best means for obtaining a diagnosis. Once this relationship is elucidated further, the treatment approach can be tailored, and patients will possibly avoid a total thyroidectomy and radioactive iodine ablation, as currently indicated for PTC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".