Procarbazine, Lomustine and Vincristine Toxicity in Low-Grade Gliomas
Bibliographic record
Abstract
Background: Procarbazine, lomustine, and vincristine (PCV) significantly improve survival outcomes in LGG (low-grade gliomas). Administration of PCV to LGG patients increased tremendously over the past years as it went from 2 patients per year between 2005 and 2012 to 23 patients in 2015 only in our centre. However, serious hematological and non-hematological adverse events may occur. The purpose of this study was to evaluate the toxicity of PCV and its clinical relevance in our practice. Methods: We retrospectively reviewed the charts of 57 patients with LGG who received PCV at the Centre hospitalier de l’Université de Montréal between 1 January 2005 and 27 July 2016. Results: Procarbazine, lomustine, and vincristine were associated with severe hematological toxicity as clinically significant grade 3 anemia, neutropenia, and thrombocytopenia occurred in 7%, 10%, and 28% of patients, respectively. Other frequent adverse events such as the increase of liver enzymes, cutaneous rash, neurotoxicity, and vomiting occurred in 65%, 26%, 60%, and 40% of patients, respectively. Patients with prophylactic trimethoprim/sulfamethoxazole had more grade 3 hematological toxicity with PCV, especially anemia (p = 0.040) and thrombocytopenia (p = 0.003) but we found no increase in PCV toxicity in patients on concurrent anticonvulsants. Patients with grade 3 neutropenia had a significantly lower survival (median survival 44.0 months vs. 114.0 months, p = 0.001). Patients who were given PCV at diagnosis had more grade 3 anemia than those who received it at subsequent lines of treatment (p = 0.042). Conclusion: Procarbazine, lomustine, and vincristine increase survival in LGG but were also associated with major hematologic, hepatic, neurologic, and cutaneous toxicity. Anti-Pneumocystis jiroveci pneumonia (PJP) prophylaxis, but not anticonvulsants, enhances hematologic toxicity.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".