Volumetric tumor control and predictors of adverse events following gammaknife stereotactic radiosurgery for intracranial meningiomas
Bibliographic record
Abstract
Introduction: Cognitive impairment and personality changes following brain tumors may be due to frontal network disruption.The effects of different tumor components such as residual tumor size, gliosis, edema and encephalomalacia on frontal behavior syndromes is unknown.The aim of our study was to determine the relation between tumor components and apathy, disinihibition and executive dysfunction, using the FrSBe, a standardized rating scale.Methods: 31 brain tumor patients who completed the FrSBe were included.Questionnaires were scored and raw scores converted to T-scores (mean 50, SD 10) according to published norms.Using OsiRIX, brain lesions were manually segmented on the Fluid attenuated inversion recovery (FLAIR) sequence into residual tumor, gliosis, edema and encephalomalacia.Spearman correlations were used to determine the relationship between tumor components and frontal behaviors as measured by FrSBe scores.Results: Clinically significant levels of Apathy were endorsed on the patient self-report and family-rating scales of the FrSBe (mean T-scoreﾱ SD: 65.19ﾱ 17.28 and 68.75ﾱ 17.57, respectively).Self-reported Executive Dysfunction was also clinically significant (68.16ﾱ 14.63).Encephalomalacia was positively correlated with family ratings of Apathy (r=0.491;p<0.045),Disinhibition (r=0.532;p<0.034), and Executive Dysfunction (r=0.583;p<0.018).None of the other features of the brain lesions showed correlations with the FrSBe.Conclusion: Family ratings of three frontal behaviors are correlated with encephalomalacia in brain tumor patients.Our results suggest that tumor components have differential effects on frontal circuits.Systematic assessment of these behaviors in brain tumor patients may provide better understanding of these differential effects, and have implications for treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".