Wrong-site craniotomy: analysis of 35 cases and systems for prevention
Bibliographic record
Abstract
OBJECT: The purpose of this case review was to identify and analyze existing wrong-site craniotomy (WSC) cases to determine the factors that contributed to the errors and to suggest preventative strategies for WSC. Wrong-site surgery (WSS) is a devastating surgical error that has gained increased public attention in recent years due to some high-profile cases. Despite the implementation of preventative methods such as preoperative checklists and surgical time-outs, WSS still occurs to this day. The clinical consequences of WSC are distinct compared with other types of WSS due to the unique function of the brain. METHODS: The authors searched medical, legal, and media databases and contacted state medical licensing boards to identify and gather information about WSC cases. The cases were reviewed and analyzed for factors that contributed to the errors. RESULTS: Four major categories of contributing factors were found: 1) communication breakdown; 2) inadequate preoperative checks; 3) technical factors and imaging; and 4) human error. The WSC cases are used to illustrate how these types of factors can precipitate the surgical error. Clinical outcomes and disciplinary actions are summarized. Obtaining information about the cases discovered was very challenging, in part because WSS reporting is inadequate. CONCLUSIONS: This case review demonstrates that a broad range of events and factors can cause human errors to breach patient safeguards and lead to a WSC; however, in essentially all cases the WSCs were preventable with strict adherence to comprehensive and thorough protocols.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".