Medication Errors: Reported Prescription Faults and Prescription Error
Bibliographic record
Abstract
The present study aimed to evaluate the trends of prescription errors that did not caused any harm to the patients and the prescription errors that were identified before reaching to the patients in the year 2017 at a tertiary care hospital in Kingdom Saudi Arabia. Simple random sampling and sampling based on prescription errors that were identified, documented, and reported before reaching the patients in the first three quarters of 2017 were performed in present observational retrospective study. Descriptive analysis with D’Agostino & Pearson omnibus were applied for normality testing at 95% CI through one-sample t-test to compare the prescription errors that did not cause harm to the patients and were identified before reaching the patient in the first quarter (Q1), the second quarter (Q2), and the third quarter (Q3) of 2017. Total number of prescription errors that did not caused harm to the patients were 1,601 in Quarter 1 further decreased to 1,422 in Quarter 2 and then increased to 1,710 in Quarter 3 of 2017. Furthermore, the total number of prescription errors that did not cause harm to the patients were 1,601 in Quarter 1 further decreased to 1,422 in Quarter 2 and then increased to 1,710 in Quarter 3 of 2017. The current study revealed that prescription errors were common in the tertiary Hospital, Taif, Saudi Arabia. Therefore, educating the prescribers to reduce prescription errors through seminars, conferences, and workshops is essential. Also, a joint training exercise for the pharmacist and doctors would minimize the prescribing errors.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".