Prognostic Factors of Low-Grade Gliomas in Adults
Bibliographic record
Abstract
Adult low-grade gliomas are a rare and aggressive pathology of the central nervous system. Some of their characteristics contribute to the patient’s life expectancy and to their management. This study aimed to characterize and identify the main prognostic factors of low-grade gliomas. The six-year retrospective study statistically analyzed the demographic, imaging, and morphogenetic characteristics of the patient group through appropriate parameters. Immunohistochemical tests were performed: IDH1, Ki-67, p53, and Nestin, as well as FISH tests on the CDKN2A gene and 1p/19q codeletion. The pathology was prevalent in females, with patients having an average age of 56.31 years. The average tumor volume was 41.61 cm3, producing a midline shift with an average of 7.5 mm. Its displacement had a negative impact on survival. The presence of a residual tumor resulted in decreased survival and is an independent risk factor for mortality. Positivity for p53 identified a low survival rate. CDKN2A mutations were an independent risk factor for mortality. We identified that a negative prognosis is influenced by the association of epilepsy with headache, tumor volume, and immunoreactivity to IDH1 and p53. Independent factors associated with mortality were midline shift, presence of tumor residue, and CDKN2A gene deletions and amplifications.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".