Wrong-site craniotomy: analysis of 35 cases and systems for prevention
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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