Epidermal Growth Factor Receptor Inhibitor–Induced Hypomagnesemia: A Survey of Practice Patterns among Canadian Gastrointestinal Medical Oncologists
Bibliographic record
Abstract
Background: The optimal management of hypomagnesemia (hMg) induced by epidermal growth factor receptor inhibitors (egfris) for advanced colorectal cancer is unclear. We surveyed gastrointestinal medical oncologists in Canada to determine practice patterns for the management of egfri-induced hMg. Methods: Based on distribution lists from the Eastern Canadian Colorectal Cancer Consensus Conference and the Western Canadian Gastrointestinal Cancer Consensus Conference, medical oncologists were invited to participate in an online questionnaire between November 2013 and February 2014. Results: From the 104 eligible physicians, 40 responses were obtained (38.5%). Panitumumab was more commonly prescribed than cetuximab by 70% of respondents, with 25% prescribing cetuximab and panitumumab equally. Most respondents obtain a serum magnesium level before initiating a patient on an egfri (92.5%) and before every treatment (90%). Most use a reactive strategy for magnesium supplementation (90%) and, when using supplementation, favour intravenous (iv) alone (40%) or iv and oral (45%) dosing. Magnesium sulfate was used for iv replacement, and the most common oral strategies were magnesium oxide (36.4%) and magnesium rougier (18.2%). Under the reactive strategy, intervention occurred at hMg grade 1 (70.3%) or grade 2 (27%). Of the survey respondents, 45% felt that 1-5 of their patients have ever developed symptoms attributable to hMg, and 35% have had to interrupt egfri therapy because of this toxicity, most commonly at grade 3 (30%) or grade 4 (45%) hMg. The most important question about egfri-induced hMg was its relevance to clinical outcomes (45%) and its symptoms (37.5%). Conclusions: In Canada, various strategies are used in the management of egfri-induced hMg, including prophylactic and reactive approaches that incorporate iv, oral, or a combination of iv and oral supplementation. Clinicians are concerned about the effect of hMg on clinical outcomes and about the symptoms that patients experience as a result of this 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".