Mitotic counts provide additional prognostic information in grade II mammary carcinoma
Bibliographic record
Abstract
The ability to predict how long a patient diagnosed with breast cancer is likely to survive is still imprecise, despite numerous studies which have identified potential prognostic markers. The "established" markers such as nodal status, tumour size, and histological grade have been used for many years and certainly provide some degree of accuracy upon which treatment can be based. However, women with similar prognostic features can vary significantly in their outcome and very few of the newly identified markers provide information that is sufficiently useful to warrant the time and expense spent on their evaluation. In a cohort of 145 women, an assessment has been made of whether knowledge of the proliferative activity of grade II infiltrating ductal breast carcinomas can improve the accuracy of predicting clinical outcome for individual patients. Use of the mitotic count (MC), which was assessed as part of the grading system, enabled patients to be stratified into "good" and "bad" prognostic groups. The measurement of S-phase fraction using flow cytometry gave a similar result, but has the disadvantage that the technique requires specialized equipment. The evaluation of Ki-67 expression using immunohistochemistry was of no additional prognostic value in this defined group. It is proposed that MC, used once to establish grade, could be used again amongst the grade II tumours to improve the accuracy of prognosis and thus influence treatment strategies with minimal additional effort or expense.
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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".