Management of Pyrexia Associated with the Combination of Dabrafenib and Trametinib: Canadian Consensus Statements
Bibliographic record
Abstract
The combination of dabrafenib and trametinib is a well-established treatment for BRAF-mutated melanoma. However, the effectiveness of this approach may be hindered by the development of treatment-related pyrexia syndrome, which occurs in at least 50% of treated patients. Without appropriate intervention, pyrexia syndrome has the potential to worsen and can result in hypotension secondary to dehydration and associated organ-related complications. Furthermore, premature treatment discontinuation may result in a reduction in progression-free and overall survival. Despite existing guidance, there is still a wide variety of therapeutic approaches suggested in the literature for both the definition and management of dabrafenib and trametinib-related pyrexia. This is reflected in the practice variation of its prevention and treatment within and between Canadian cancer centres. A Canadian working group was formed and consensus statements were constructed based on evidence and finalised through a two-round modified Delphi approach. The statements led to the development of a pyrexia treatment algorithm that can easily be applied in routine practice. The Canadian working group consensus statements serve to provide practical guidance for the management of dabrafenib and trametinib-related pyrexia, hopefully leading to reduced discontinuation rates, and ultimately improve patients’ quality of life and cancer-related outcomes.
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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 | 0.062 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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".