Reporting of near‐miss events for transfusion medicine: improving transfusion safety
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: Half of the reported serious adverse events from transfusion are a consequence of medical error. A no-fault medical-event reporting system for transfusion medicine (MERS-TM) was developed to capture and analyze both near-miss and actual transfusion-related errors. STUDY DESIGN AND METHODS: A prospective audit of transfusion-related errors was performed to determine the ability of MERS-TM to identify the frequency and patterns of errors. RESULTS: Events and near-miss events (total, 819) were recorded for a period of 19 months (median, 51/month). No serious adverse patient outcome occurred, despite these events, with the transfusion of 17,465 units of RBCs. Sixty-one events (7.4%) were potentially life-threatening or could have led to permanent injury (severity Level 1). Of most concern were 3 samples collected from the wrong patient, 13 mislabeled samples, and 22 requests for blood for the wrong patient. Near-miss events were five times more frequent than actual transfusion errors, and 68 percent of errors were detected before blood was issued. Sixty-one percent of events originated from patient areas, 35 percent from the blood bank, and 4 percent from the blood supplier or other hospitals. Repeat collection was required for 1 of every 94 samples, and 1 in 346 requests for blood components was incorrect. Education of nurses and alterations to blood bank forms were not by themselves effective in reducing severe errors. An artifactual 50-percent reduction in the number of errors reported was noted during a 6-month period when two chief members of the event-reporting team were on temporary leave. CONCLUSION: The MERS-TM allowed the recognition and analysis of errors, determination of patterns of errors, and monitoring for changes in frequency after corrective action was implemented. Although no permanent injury resulted from the 819 events, innovative mechanisms must be designed to prevent these errors, instead of relying on faulty informal checks to capture errors after they occur.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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