Optimization of an imaging protocol for stage I seminoma surveillance based on variations in relapse location over time.
Bibliographic record
Abstract
475 Background: Surveillance is recommended for patients with stage I seminoma post orchidectomy but CT imaging involves ionising radiation, with risk of associated secondary malignancies. We assessed site of disease relapse during surveillance to guide development of a risk adapted imaging protocol. Methods: Data was obtained from a prospectively maintained database of patients with stage I seminoma on surveillance after orchidectomy from 1981-2011. Relapse was determined by clinical and/or radiographic finding with or without pathological confirmation or tumour marker elevation. Results: 753 patients were identified. The median age at orchidectomy was 33.7 years. With a median follow up of 10.5 years, range 1.1-30.1, 115 (15.3%) patients relapsed. Relapse was detected radiologically in 114 (99.1%), with 9 (7.8%) having simultaneously elevated tumour markers. A clinical diagnosis of relapse was made in 1 case (inguinal node – 0.9%). The location and time to relapse are shown in table. Conclusions: In stage I seminoma surveillance, pelvic nodal relapse was restricted to the early period of follow up. Excluding the pelvis during CT imaging after the third year of surveillance may optimise the detection of relapse whilst minimising total radiation exposure. This has now been adopted at our centre since 2011 without any subsequent late pelvic relapses. [Table: see text]
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".