Prognostic factors in adults with primary intracranial ependymoma: a population- based study
Bibliographic record
Abstract
Abstract INTRODUCTION Ependymomas are rare central nervous system neoplasms associated premature mortality. National registry-based datasets are therefore useful for identification of prognostic factors for adults with intracranial ependymoma. We aimed to assess demographic and clinical factors associated with survival for adults with intracranial ependymoma. METHODS The Surveillance, Epidemiology and End Results registry was queried for prognostic factors and survival outcomes of adult (≥ 18 years) patients diagnosed with intracranial ependymoma from 2004–2018. RESULTS We identified 183 adults with primary intracranial ependymoma; 89 patients (49%) had WHO grade II tumors and 94 patients (51%) had WHO grade III tumors. Increased age (HR: 1.03, 95% CI: 1.01–1.04), higher tumour grade (HR: 2.14 95% CI: 1.31–3.48) and tumor location (HR: 1.69, 95% CI: 1.04–2.75) were significant predictors of mortality. Adjuvant therapy and extent of resection were not predictive of overall survival. In anatomic subgroups, age (p < 0.001), sex (p = 0.022), and tumor grade (p = 0.011) were associated with overall survival among supratentorial ependymoma cases, whereas extent of surgery (p = 0.035) was significant for infratentorial ependymomas survival. CONCLUSION Leveraging large national registry data, our findings add to the body of evidence informing clinical decision making for adults with intracranial ependymoma.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".