The Surveillance After Extremity Tumor Surgery (SAFETY) Pilot International Multi-Center Randomized Controlled Trial
Bibliographic record
Abstract
Soft-tissue sarcomas (STSs) are rare malignancies predominantly found in the extremities. Surgery and radiation are standard treatments, but post-operative pulmonary surveillance, involving clinical visits and thoracic imaging, is crucial due to a high recurrence rate, most commonly to the lungs. Current pulmonary surveillance guidelines lack robust evidence. The Surveillance AFter Extremity Tumor SurgerY (SAFETY) randomized controlled trial is designed to determine the impact of pulmonary surveillance frequency (every three versus six months) and chest imaging modality (CXR versus CT) on patient-important outcomes. The pilot phase assessed feasibility of patient enrolment, protocol adherence, and data quality, as well as aggregate outcomes at two years of follow-up. 100 patients were enrolled from 300 screened patients across 17 international sites. Minor protocol deviations were common. Follow-up, data completeness and data quality met the progression criteria of 85%. Of the 100 patients, 15 died, 21 had metastases, seven had local recurrence and 30 experienced at least one serious adverse event. This SAFETY trial study established feasibility of enrolment and data quality, and confirmed the need to emphasize protocol adherence in sarcoma care. The results of this trial are expected to provide crucial evidence to standardize STS pulmonary surveillance practices, ultimately improving patient management and expectations.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".