Genetic Events in the Evolution of Thyroid Cancer
Bibliographic record
Abstract
OBJECTIVE: To delineate the evolution of thyroid cancer using the Volgelstein model of cancer evolution and to demonstrate the genetic "hits" in the development of undifferentiated cancer from normal thyroid cells. METHOD: Immunohistochemical and molecular biologic techniques were used to delineate the prevalence of (1) the ras oncogene, (2) p53, and (3) the ret/PTC oncogene in the development of differentiated thyroid cancer from normal cells and hyperplasia. The evolution of differentiated thyroid cancer to the dedifferentiated or anaplastic type was also investigated. The methodology used was standard immunohistochemical staining techniques as well as polymerase chain reaction technology for the elucidation of these various oncogenes and tumour suppressor genes. RESULTS: We have demonstrated that there is a high preponderance of ras and ret/PTC oncogenes in the evolution of thyroid cancer. Further along the continuum, p53 has been demonstrated to be prevalent in the development of dedifferentiated thyroid cancer (i.e., tall cell and insular variety as well as anaplastic cancer). CONCLUSION: Several genetic hits have been demonstrated to be prevalent in the evolution of thyroid cancer. These are preliminary oncogenic maps, and further work in this area will help to establish a definite biologic pattern in the development of this malignancy.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".