The C-Arm Technique to Locate a Lost Needle During Robotic Gynecology Surgery
Bibliographic record
Abstract
The loss of a needle during robotic surgery can be a potentially harmful medical event, especially if retained. While the occurrence of such an event is uncommon, loss of a needle can cause a significant challenge to find and retrieve. Failure to find a lost needle can also have tremendous medicolegal consequences, as a result, this issue is classified as a "never event". There is currently no standardized process for finding a lost needle during a robotic gynecologic operation. The objective of this report was to review the current literature on lost surgical needles and present a case that utilized a mobile C-arm fluoroscopy to triangulate a lost needle during robotic surgery. Although the use of C-arm fluoroscopy has been noted in the literature, the technique has not been described in detail. We describe a safe and efficient way to find lost needles intraoperatively that can be integrated into a standardized protocol. J Clin Gynecol Obstet. 2023;12(3):93-97 doi: https://doi.org/10.14740/jcgo910
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".