THE UTILITY OF INTRAOPERATIVE MARROW MARGIN FROZEN SECTION IN EXTREMITY BONE SARCOMA RESECTION
Bibliographic record
Abstract
Achieving negative margins in the surgical management of bone sarcomas is paramount. Intraoperative frozen section assessment of marrow margins remains the most widely utilized tool to assess bony margins intraoperatively in extremity bone sarcomas. However, frozen sections are technically challenging, resource intensive, and may be of limited utility in the modern era of magnetic resonance imaging. The purpose of the current study was to 1) evaluate the accuracy of intraoperative frozen section marrow margins compared to final pathologic assessment, 2) identify how frozen section analysis compares to gross intraoperative marrow margin assessment, and 3) determine how positive intraoperative frozen sections impact surgical decision making. Consecutive patients from 2010 to 2022 undergoing surgical resection of extremity bone sarcomas that had intraoperative marrow margin frozen section were included for review. The accuracy of both intraoperative frozen section and intraoperative gross margin assessment was compared to the final pathologic assessment utilizing positive predictive values (PPV) and negative predictive value (NPV) with 95% confidence intervals (CI). Changes in surgical decision making was recorded for patients with positive intraoperative margins. A total of 175 intraoperative frozen section marrow margins in 168 patients with bone sarcomas of the extremities were included. There were four positive intraoperative frozen section margins with two false positives and two false negatives when compared to final pathologic assessment. Gross intraoperative assessment yielded no false positives and 2 false negatives compared to final pathologic assessment. This yielded a PPV of 50% (95% CI 15.6% - 84.4%) and NPV of 98.8% (95% CI, 96.9% - 99.6%) for the frozen section evaluation compared to a PPV of 100% (95% CI, 15.8% -100%) and NPV of 98.8% (95% CI, 97.0% - 99.6%) for gross intraoperative evaluation. Positive intraoperative frozen section margins led to further bony resection in three of the four cases. Frozen section marrow margin assessment did not provide additional clinical value beyond gross marrow margin assessment intraoperatively in extremity bone sarcoma resections. Frozen section assessment demonstrated a low PPV and led to unnecessary additional bone resections. The results of the current study demonstrate that examination of gross marrow margins intraoperatively, in conjunction with pre-operative advanced imaging, is an adequate method for margin assessment and may save time and resources.
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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".