Discordance of oncologic surgical classifications in COG studies.
Bibliographic record
Abstract
10528 Background: “Wide resection” (a cuff of normal tissue) versus “radical resection” (the entire compartment) indicate very distinct oncologic surgical procedures and hence potential margin status. Distinguishing between these two oncologic classifications is important for understanding oncologic outcomes. Methods: We examined the available data for COG AOST0331: A Randomized Trial of the European and American Osteosarcoma Study Group to Optimize Treatment Strategies for Resectable Osteosarcoma Based on Histological Response to Pre-Operative Chemotherapy. We reviewed the surgical and pathology reports of patients considered to have received a wide or radical resection according to the investigator at the patient’s institution. Results: In 956 patients, the overall discordance rate was 43%. Of those patients reported to have had a wide excision by the reporting institution, only 5% of patients were reclassified to radical resection. However, of those patients reported to have undergone radical resection, 75% were reclassified to wide. In the absence of this re-review of the data, 56% of the patients would have been reported to have had a radical resection when in fact only 17% met the criteria for true radical resection. The greater number of patient reclassified to wide from radical is likely indicative of the influence and limits of the CPT coding system currently used for billing by surgeons; only CPT codes for radical resection currently exist. Conclusions: These discrepancies complicate subsequent data analysis, particularly assessing predictors of local recurrence. While this reporting problem is not unique to COG studies, we aim to use this example to raise awareness among our colleagues about the importance of accurate data reporting. Providing better training to those individuals responsible for submitting data may decrease the incidence of erroneous data entry and is essential to the successful completion of study objectives.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".