Wrong-site nerve blocks: evidence-review and prevention strategies
Bibliographic record
Abstract
PURPOSE OF REVIEW: There has been increasing attention to wrong site medical procedures over the last 20 years. This review aims to provide a summary of the current understanding and recommendations for the prevention of wrong-site nerve blocks (WSNB). RECENT FINDINGS: Various procedural, patient, practitioner, and organizational factors have been associated with the risk of WSNB. Recent findings have suggested that the use of a checklist is likely to reduce the incidence of WSNB. However, despite the widespread use of preprocedural checklists, WSNB continue to occur at significant frequency. This may be due to the inability of practitioners and teams to implement checklists correctly or the cognitive errors that prevent checklists from being executed as designed. SUMMARY: Though the evidence is limited, it is recommended that a combination of multiple strategies should be employed to prevent WSNB. These include the use of preprocedural markings, well constructed checklists, time-out/stop-moments, and cognitive/physical aids. Effective implementation requires team education and engagement that empowers all team members to speak up as part of a culture of safety.
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.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".