Is Detection of Relapse by Surveillance Imaging Associated With Longer Survival in Patients With Rhabdomyosarcoma?
Bibliographic record
Abstract
BACKGROUND: We investigated whether surveillance imaging had an impact on post-relapse survival in patients with rhabdomyosarcoma (RMS). We hypothesized that relapse detected by imaging (group IM) would be associated with longer survival compared with relapse detected with a clinical sign or symptom (group SS). MATERIALS AND METHODS: We performed an observational multi-institutional study in 127 patients with relapsed RMS comparing overall survival (OS) after relapse using Kaplan-Meier and Cox proportional hazards analyses. RESULTS: Relapse was detected in 60 (47%) group IM and 67 (53%) SS patients. Median follow-up in survivors was 4 years (range 1.0 to 16.7 y). Four-year OS rates were similar between group IM (28%, 95% confidence interval [CI]: 14%-40%) and SS (21%, 95% CI: 11%-31%) ( P =0.14). In multivariable analyses accounting for institution, age at diagnosis, time to relapse, risk group at diagnosis, and primary site, not receiving chemotherapy (hazard ratio [HR]: 6.8, 95% CI: 2.8-16.6), radiation (HR: 3, 95% CI: 1.7-5.3), or surgery (HR: 2.8, 95% CI: 1.6-4.8) after relapse were independently associated with poor OS. CONCLUSION: These results on whether surveillance imaging provides survival benefit in patients with relapsed RMS are inconclusive. Larger studies are needed to justify current surveillance recommendations. Chemotherapy, radiotherapy and surgery to treat recurrence prolong OS.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".