Progesterone and Estrogen Receptors in Meningiomas – A Clinicopathological Analysis
Bibliographic record
Abstract
Background – Meningiomas are common tumors of the Central Nervous System. Recurrence in meningiomas has been proven to be associated with factors like extent of resection and grade of tumor. Decrease in progesterone receptor expression has been linked to increased rate of recurrence. We undertook this study to know about progesterone and estrogen receptor expression in meningiomas and its association with different clinicopathological variables as data is lacking in the Indian population. Materials and Methods – Meningiomas operated in a tertiary referral centre of North Kerala, India in 2 years were taken into the study. The tumors were graded and immunohistochemistry was performed using antibodies to Estrogen and progesterone receptors (ER&PR) and Ki-67. The expression of PR and ER was correlated with clinicopathologic variables. The patients were followed up for 2 years. Results – Grade I and grade II tumors constituted 85.3% and 14.7% respectively of the total number of meningioma cases. There were no grade III tumors in the series. The average PR positivity in grade I tumors (60.77%) was higher than in grade II tumors (46.88%). There was no gender related difference in PR staining. ER positivity was found only in a few cases. Conclusion – PR deterioration was associated with increased cell turnover. Meningiomas occurring in this study population are similar in clinicopathological parameters to those tumors occurring in other parts of the country but different in some aspects like grade from tumors occurring in other parts of the world. Strict follow up of a larger cohort of patients for a longer time period will be required to draw conclusions about prognosis in this population.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".