Bibliographic record
Abstract
The management of patients harboring central nervous system (CNS) hemangiopericytomas (HPCs) is a partially answered challenge. These are rare locally aggressive lesions, with potential for local recurrence, distal neural metastasis (DNM), and extraneural metastasis (ENM). Resection, when feasible, remains the initial treatment option, providing histological diagnosis and immediate relief of tumor-related mass effect. Patients receiving surgery alone or surgery and external beam radiotherapy (EBRT) show improved overall survival (OS) and progression-free survival as compared to those undergoing a biopsy alone (p = 0.01 and p = 0.02, respectively). Yet, in many instances, patient and tumor-related parameters preclude complete resection. EBRT or stereotactic radiosurgery (SRS) shares a significant role in achieving local tumor control, not shown to impact OS in HPC patients. The benefits of SRS/EBRT are clearly limited to improved local tumor volume control and neurologic function, not affecting DNM or ENM development. SRS provides acceptable rates of local tumor volume control coupled with treatment safety and a patient-friendly apparatus and procedure. Single-session SRS is most effective for lesions measuring <2 cm in their largest diameter (10 cm3 volume), with prescription doses of at >15 Gy. Systemic HPC disease is managed with various chemotherapeutic, immunotherapeutic, and anti-angiographic agents, with limited success. We present a short discussion on CNS HPCs, focusing our discussion on available evidence regarding the role of microsurgical resection, EBRT, SRS, chemotherapy, and immunotherapy for upfront, part of adoptive hybrid surgery approach or for recurrent HPCs.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".