Unplanned Sarcoma Excisions: Understanding How They Happen
Bibliographic record
Abstract
BACKGROUND: Soft-tissue sarcomas present as a mass with nonspecific symptoms, and unplanned excisions commonly occur. The purpose of this study was to analyze the incidence of unplanned excisions performed by orthopaedic surgeons and to conduct a root cause analysis (RCA) of the steps that led to unplanned excisions in all the cases. METHODS: A retrospective case-control study was conducted. Two cohorts were identified, one including patients who underwent an unplanned excision of a soft-tissue sarcoma (n = 107) and a second cohort with patients whose entire care was performed at our sarcoma center (n = 102). A RCA was conducted with the whole sample to identify the preventable causes that led to sarcoma unplanned excisions. RESULTS: Orthopedic surgeons were the second group of physicians to perform the most unplanned excisions, only behind general surgeons. Inadequate imaging was encountered in 76.6% of the patients (n = 82, 95% confidence interval, 67.8 to 83.6). Forty-five patients (42.1%) had no imaging studies before the unplanned procedure. In the RCA, the most notable obstacles found were (1) incorrect assumption of a benign diagnosis, (2) failure to obtain the appropriate imaging study, (3) incorrectly reported imaging studies, (4) failure to order a biopsy, and (5) incorrect reporting of the biopsy. CONCLUSIONS: Despite educational efforts, unplanned excisions and the devastating consequences that sometimes follow continue to occur. Orthopaedic surgeons persist in playing a role in the unplanned procedure burden.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".