The utility of intraoperative marrow margin frozen section in extremity bone sarcoma resection
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intraoperative frozen section analysis is commonly used to evaluate marrow margins during extremity bone sarcoma resections, but its efficacy in the era of magnetic resonance imaging is debated. This study aimed to compare the accuracy of intraoperative frozen section assessment with final pathology, assess its correlation with gross intraoperative margin assessment, and evaluate its impact on surgical decision making. METHODS: Consecutive patients undergoing extremity bone sarcoma resections from 2010 to 2022 at a single sarcoma center were included. Intraoperative frozen section and gross margin assessments were compared to final pathology using positive predictive values (PPV) and negative predictive values (NPV). Changes in surgical decisions due to positive intraoperative margins were recorded. RESULTS: Of 166 intraoperative frozen section marrow margins, four were indeterminant/positive, with two false positive/indeterminant findings and two false negatives compared to final pathology. Gross intraoperative assessment had no false positives and two false negatives. Frozen section analysis yielded a PPV of 50% (95% confidence interval [CI]: 16%-84%) and NPV of 98.8% (95% CI: 97%-100%), while gross assessment had a PPV of 100% (95% CI: 16%-100%) and NPV of 98.8% (95% CI: 97%-100%). Positive frozen section margins led to additional resections in three of four cases. CONCLUSIONS: Intraoperative frozen section analysis did not offer added clinical value beyond gross margin assessment in extremity bone sarcoma resections. It exhibited a low PPV and led to unnecessary additional resections. Gross intraoperative assessment proved adequate for margin evaluation, potentially saving time and resources.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".