Bibliographic record
Abstract
OBJECTIVE: Surgical error is common and contributes to complications for patients, necessitating detailed prospective collection and analysis of error data that emphasizes prevention. METHODS: One neurosurgeon prospectively recorded errors and complications for consecutive patients undergoing elective neurosurgical procedures. Each error was scored for type, severity, preventability, and consequence. RESULTS: Between May 2000 and August 2006, 1108 elective cases were studied, comprising 76.1% cranial, 22.7% spinal, and 1.2% other procedures. There were 2684 errors in 87.1% of cases. The most common errors were technical (27.8%), contamination (25.3%), equipment failure or missing equipment (18.2%), or related to delay (12.5%). Of the errors, 22.6% were considered major and 77.4% were minor, with 2.7% of errors substantially impacting the clinical course of the patient. Of all errors, 78.5% were deemed preventable. Of the complications, 16.7% were related to errors, of which 80.6% were major errors. Of the error-related complications, 74.2% were declared preventable. A propensity for error was identified with cranial procedures and patients with higher American Society of Anesthesiologists scores (P < 0.01). CONCLUSION: Surgical errors are common, often preventable, and frequently lead to clinical impact. In addition, the type of procedure and characteristics of the patient are important factors to consider when addressing surgical error. For individual surgeons to maintain quality control and contribute to the safety of the health care system, they must track and analyze errors to ensure that systems may be developed to prevent their occurrence.
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 | 0.022 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".