Medication errors on oral chemotherapy in children with acute lymphoblastic leukemia in a developing country
Bibliographic record
Abstract
BACKGROUND: Medication errors occur universally. Inappropriate administration of chemotherapy drugs can have adverse effects in cancer patients. Our objective was to assess the rate and type of medication errors in children with acute lymphoblastic leukemia (ALL) receiving oral chemotherapy in outpatient setting. PROCEDURE: Prescription and administration of oral chemotherapy drugs in children with ALL were evaluated prospectively to determine rate and type of medication errors. Errors were defined as prescription (physician) level or administration (patient) level errors. RESULTS: Two hundred eighty-nine drugs were prescribed to 121 patients. Medication errors occurred in 36 (12.5%) prescriptions; 21(7.3%) were administration errors, 13 (4.5%) were prescribing errors, and two errors occurred at both levels. Mercaptopurine (6-MP) was significantly associated with higher rates of errors (Odds ratio [OR] = 2.1, 95% CI [confidence interval] 1-4.1) whereas lapses were less with dexamethasone (OR = 0.25, 95% CI 0.09-0.67). As a result of medication errors 28 (23.1%) patients received inappropriate doses. Twenty five (21%) patients received sub-optimal doses whereas three got higher doses of chemotherapy. On univariate analysis, socioeconomic status, education status of the caregiver, 6-MP and methotrexate were significantly associated with errors (P ≤ 0.05). On multivariate analysis, ≤ primary school education of the caregiver and prescription of methotrexate were independent predictors of errors. CONCLUSIONS: Medication errors affected nearly one fourth of the children receiving oral chemotherapy. Future studies are needed to look at effective interventions to avoid chemotherapy associated errors especially amongst the lower strata of society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".