Medication Safety: Occurrence and Impact of Unanticipated Variation in Intravenous Methotrexate Dosing
Bibliographic record
Abstract
BACKGROUND: Studies using direct measurement suggest that the doses of up to 65% of drug infusions are outside industry standards. These preparation-associated errors occur despite routine safety procedures. As of April 5, 2006, the clinical impact of these errors had not been evaluated. OBJECTIVE: To measure the occurrence and associated clinical outcomes of variations in intravenous methotrexate dosing. METHODS: A prospective observational study was performed on 47 methotrexate infusions of 800 mg/m2 that were administered to 19 children with acute lymphoblastic leukemia. Serum methotrexate concentrations were measured at the end of the infusions, which were administered over 24 hours. The total methotrexate dose was determined by direct measurement of the concentration and the volume of each infusion. RESULTS: Dosing errors greater than or equal to 10% occurred in 11 (23%) infusions and ranged from -61% to 55% of the ideal dose. Repeated measures regression analysis found the measured total methotrexate dose was not significantly associated with the serum methotrexate concentration (p = 0.58) or with clinical toxicities. The methotrexate dose administered over the last hours of infusion (p = 0.006) and the serum creatinine level at diagnosis (p = 0.05) were the most significant predictors of the methotrexate concentration. High methotrexate concentrations were significantly associated with increased hepatic aminotransferase levels; however, the degree of elevation was of limited clinical relevance. CONCLUSIONS: While unexpected errors in drug dosing are more common than is suggested by other methods, the clinical impact observed in this model of methotrexate infusion was not demonstrably greater than medication errors described by other methods. Subsequent studies in this model of dosing error will require larger sample sizes, and other drugs should be evaluated.
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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 | 0.005 | 0.045 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".