Surgical Considerations in Treating Central Nervous System Lymphomas: A Case Series of 11 Patients
Bibliographic record
Abstract
In this retrospective unicentric study, we analyzed the medical records of 11 patients who were surgically treated for CNS lymphoma, both primary and secondary, between 2009 and 2024. Given the rarity of CNS lymphomas and their diverse signs and symptoms based on tumoral location, our aim was to describe key aspects, such as clinical presentations and surgical management. A possible relationship between obesity and CNS lymphoma progression was investigated through an analysis of previous study findings. The literature suggests a wide spectrum of manifestations, from nausea and headaches to loss of equilibrium and speech impairment. A predominance of unsystematized balance disorders and epileptic seizures were affirmed. Notably, as emerged from our study, aphasia was a particularly interesting neurological symptom due to its rarity in the clinical features of CNSL. Other significant factors, such as tumor localization and perioperative phases, were thoroughly investigated, with the latter highlighted by an illustrative case report. Additionally, a literature review was included, comprising nine recent retrospective studies on the efficacy of surgical resection for patients diagnosed with PCNSL.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".