Incidence, causes, and consequences of preventable adverse drug events: protocol for an overview of reviews
Bibliographic record
Abstract
Abstract Background Medication errors represent a noteworthy source of harm to patients. In recent years, several systematic reviews have assessed the frequency and causes of these events, as well as other factors such as commonly associated drugs, their incidence in different specialties, and their consequences to patients. Despite this past literature, there remains a need to study discrepancies between these reviews and establish the current state of the evidence. The planned review will bring together, compare, and contract existing evidence related to the occurrence of medication errors in acute and continuing/long-term care settings. Methods A systematic review of reviews will be performed. A literature search designed by an experienced information specialist will be carried out in Medline, Embase, and the Cochrane Library. We will seek systematic reviews and meta-analyses of primary research studies that evaluate one or more of the following aspects of the occurrence of preventable adverse drug events in hospitals and long-term care centers: the incidence of preventable adverse drug events, either overall or within subgroups of interest related to setting; drug or patient characteristics; cited consequences of these events to patients, including death, emergency room visits, or other outcomes; and established causes of the preventable adverse drug events. Two researchers will independently screen all abstracts and full texts for study selection and subsequently perform data extraction from all included studies. Quality of the reviews will be assessed using the assessing the methodological quality of systematic reviews (AMSTAR) tool. Where objectives from two or more reviews overlap, we will employ the Jadad framework to assess the causes of any noted discrepancies between reviews. An overall summary of results will be performed using tabular and graphical approaches and will be supplemented by narrative description. Discussion This overview will help synthesize the broad degree of information available on this important topic. This review is being performed by members of the Drug Safety and Effectiveness Network along with collaboration from Health Canada, and its findings will be published in a peer-reviewed journal. The results may also inform future research in this area. Systematic review registration PROSPERO CRD42016043220
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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 | 0.076 | 0.101 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.017 |
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".