Medication Errors: Scope and prevention strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Medication errors are a significant public health concern. Although significant advances have been made, errors are still relatively common and represent an opportunity for healthcare improvement.Methodology/Principal Findings: Since the publication of To Err is Human, medication errors have been under tremendous scrutiny. Organizations have moved towards a non-punitive approach to evaluating errors. This approach to medication errors has aided in identifying common pathways to medication errors and improving understanding regarding the anatomy of a medication error. As a result, prevention strategies have been developed to target common themes contributing to errors. Error prevention strategies may target common contributors of medication errors, broadly grouped as performance lapses, lack of knowledge, and lack or failure of safety systems. Strategies to thwart medication errors range from process improvement to integration of technology in the health care environment.Conclusions/Significance: Organizations should devote resources to address medication error prevention strategies in an effort to improve patient outcomes and decrease morbidity and mortality associated with medication errors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it