Bibliographic record
Abstract
BACKGROUND: Medication errors with oncology drugs can place cancer patients at risk for adverse events or death. Look-alike, sound-alike (LASA) drug names may increase the risk for errors. Published lists of LASA drug names are generally a result of voluntarily reported medication incidents. This study performed a proactive review of the oncology drug formulary from the Cancer Services of the Alberta Health Services for LASA drug pairs. METHODS: The Levenshtein Distance and Bigram Similarity algorithms, same first and last letters, and Lexi-Comp(R) on-line alerts were used to review the outpatient oncology formulary to identify potential LASA generic drug name pairs. RESULTS: indicate there are more potential LASA generic drug name pairs in the oncology formulary than are published in the literature. The risk detection methods used in this study identified unique and common LASA drug pairs. The Bigram Similarity algorithm identified 186 LASA drug pairs from 3320 possible pairs. The Levenshtein Distance algorithm, same first and last letters, and Lexi-Comp(R) methods identified 42, 75, and 38 LASA drug pairs, respectively. Five generic LASA drug pairs were identified in common by all four of the risk determination methods. DISCUSSION: LASA drug pairs identified by three or four methods were considered to provide the highest risk for errors. A step-wise approach to risk reduction, dependent on the number of detection methods identifying a pair, is presented. CONCLUSION: For specialty areas of practice, a proactive system of reviewing LASA drug name pairs may be warranted for increasing medication safety.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".