Medication Misadventures Among COVID-19 Patients in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Due to the need for early and effective medications for coronavirus disease (COVID-19), less attention may have been paid to medication safety during this pandemic. OBJECTIVES: This study aimed to examine the incidence, nature, and seriousness of medication errors (MEs) and adverse drug reactions (ADRs) among hospitalized patients with COVID-19. MATERIALS AND METHODS: This is a retrospective study of MEs and ADRs reported at the King Saud Medical City (KSMC) between April 2020 and September 2020. RESULTS: A total of 343 MEs and 416 ADRs were reported during the study period. The incidence of MEs was 19% (19/100). Seventy-five MEs (21.5%) reached the patient but did not cause any harm. Wrong dose (n=101, 29.4%) was the most common type of MEs. Physicians were the most common source of MEs (87.5%). Antibiotics (32%) and antineoplastics (25%) were the most common drug categories involved in MEs and ADRs, respectively. Thirty-nine percent (n=163) of the ADRs were of serious nature. 24% (n=100) required hospitalization, 5% (n=21) were life-threatening, 16 (3.8%) required intervention to prevent permanent impairment or damage, and 6.2% (n=26) resulted in the discontinuation of treatment. CONCLUSION: The reporting of MEs appears to be high among COVID-19 patients in a large tertiary care setting in the Kingdom of Saudi Arabia (KSA). The majority of MEs were caused by dosing errors and errors in drug frequency, mostly ascribed to physicians, which may be indicative of burnout or stress among them. The reporting of MEs and ADRs can be improved by providing incentives to healthcare professionals (HCPs) and promoting a non-punitive culture. Further studies should explore the clinical consequences of medication misadventures in hospitalized COVID-19 patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".