Does Intensity of Surveillance Affect Survival After Surgery for Sarcomas? Results of a Randomized Noninferiority Trial
Bibliographic record
Abstract
BACKGROUND: Whether current postoperative surveillance regimes result in improved overall survival (OS) of patients with extremity sarcomas is unknown. QUESTIONS/PURPOSES: We hypothesized that a less intensive followup protocol would not be inferior to the conventional followup protocol in terms of OS. We (1) assessed OS of patients to determine if less intensive followup regimens led to worsened survival and asked (2) whether chest radiograph followup group was inferior to CT scan followup group in detecting pulmonary metastasis; and (3) whether less frequent (6-monthly) followup interval was inferior to more frequent (3-monthly) followup in detecting pulmonary metastasis and local recurrence. METHODS: A prospective randomized single-center noninferiority trial was conducted between January 2006 and June 2010. On the basis of 3-year survival of 60% with intensive, more frequent followup, 500 nonmetastatic patients were randomized to demonstrate noninferiority by a margin (delta) of 10% (hazard ratio [HR], 1.36). The primary end point was OS at 3 years. The secondary objective was to compare disease-free survival (DFS) (time to recurrence) at 3 years. At minimum followup of 30 months (median, 42 months; range, 30-81 months), 178 deaths were documented. RESULTS: Three-year OS and DFS for all patients was 67% and 52%, respectively. Three-year OS was 67% and 66% in chest radiography and CT groups, respectively (HR, 0.9; upper 90% confidence interval [CI], 1.13). DFS rate was 54% and 49% in chest radiography and CT groups, respectively (HR, 0.82; upper 90% CI, 0.97). Three-year OS was 64% and 69% in 6-monthly and 3-monthly groups, respectively (HR, 1.2; upper 90% CI, 1.47). DFS was 51% and 52% in 6-monthly and 3-monthly groups, respectively (HR, 1.01; upper 90% CI, 1.2). Almost 90% of local recurrences were identified by patients themselves. CONCLUSIONS: Inexpensive imaging detects the vast majority of recurrent disease in patients with sarcoma without deleterious effects on eventual outcomes. Patient education regarding self-examination will detect most instances of local recurrence although this was not directly assessed in this study. Although less frequent visits adequately detected metastasis and local recurrence, this trial could not conclusively demonstrate noninferiority in OS for a 6-monthly interval of followup visits against 3-monthly visits. LEVEL OF EVIDENCE: Level I, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".