Cost, Operative Delay, and X-Rays for Incorrect Surgical Counts
Bibliographic record
Abstract
At Cleveland clinic, an incorrect surgical count triggers Code Rust; a protocol that mandates an intraoperative patient X-ray, staff radiology read, and discussion with the surgeon before the incision is closed. Code Rust calls from November 2014 to December 2022 were retrospectively reviewed. Realtime workflow and operative details of Code Rust cases were analyzed.1277 Code Rusts were identified. Average time from ordering the X-ray to final radiology report was 50 minutes, totalling $2,362,450.00 spent on operating room time. Code Rust was called twice as frequently during urgent or emergent cases, compared to elective. There were more staff in Code Rust rooms compared to non-Code Rust rooms. A foreign body on X-ray was identified in 42/1277 (3.3%) cases. Code Rust is a resource intensive process that is more common in emergent cases that involve multiple staff. While retained foreign bodies are identified in a small percentage of cases, the current system should be revisited to reduce operating time and expense.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".