CLRM-20 IDENTIFYING RISK FACTORS AND ANALYZING SURVIVAL FOLLOWING PACHYMENINGEAL FAILURE
Bibliographic record
Abstract
Abstract OBJECTIVE Neurosurgery (NS) is an essential modality for large brain metastases (BM). As an adjuvant treatment, stereotactic radiosurgery (SRS) reduces neurocognitive toxicity without affecting post-treatment overall survival (OS) compared to whole brain radiation therapy. Pachymeningeal failure (PMF) beyond the SRS field is a relatively newly described entity, distinct from classical leptomeningeal failure (LMF), and unique to postoperative patients treated with adjuvant SRS. We sought to identify risk factors for PMF in patients treated with NS+SRS. METHODS We reviewed a prospective registry (2009 to 2020) and identified all patients treated with NS+SRS. Clinical, radiological, pathological and treatment factors were analyzed. PMF incidence was evaluated using a competing risks model and differences between cohorts were measured using the Fine-Gray method. RESULTS 144 Patients were identified. Median age was 62 (23-90). PMF occurred in 22.2% (32/144) patients). Univariate analysis indicated female gender (HR 2.65, p=0.013), higher GPA status (HR 2.4, p<0.001), absence of prior radiation therapy (HR N/A, p=0.018), controlled extracranial disease (CED) (HR 3.46, p=0.0038), and contact with the pia/dura (HR 3.30, p=0.0053) as risk factors for PMF. Piecemeal (vs En-bloc) resection also trended towards correlation (HR 2.07, p=0.054). Multivariate Analysis identified contact with pia/dura (HR 3.51, p=0.0053), piecemeal resection (HR 2.38, p=0.027), and CED (HR 3.97, p=0.0016) as significant correlates to PMF. PMF correlated with reduced OS (HR 2.90, p<0.001) but was improved compared to patients who developed LMF (HR 10.15, p= p<0.001). CONCLUSIONS PMF is an underrecognized phenomenon that correlates with pre-operative pia/dura contact and piecemeal resection in patients treated with NS+SRS for BM. While less morbid than LMF, it is a critical event that deserves increased vigilance and analysis.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".