Sci‐AM1 Sat ‐ 03: Comparison of chest radiographs, fluoroscopy and seed‐migration detector for the detection of embolized seeds to the lung
Bibliographic record
Abstract
To evaluate the efficacy of a seed‐migration detector and to compare it to fluoroscopy and to the postoperative chest radiographs generally recommended. A gamma scintillation survey meter was converted to a seed‐migration detector. It was used to perform a chest evaluation on 155 patients (8717 seeds) at their first postoperative visit. When the detector showed activity around a patient's chest, it was confirmed by taking an antero‐posterior chest radiograph and by looking at the region with fluoroscopy. 3 patients (21.3%) present at least one embolized seed. That is a 0.47% seed migration rate (41/8717). 37 (90%) of the seeds were visible under fluoroscopy and 28 (68%) appeared on x‐rays. Rapid movement of the seeds, due to breathing or to a location close to the heart, makes nine seeds to be visible with fluoroscopy but not on the radiograph. Moreover, four seeds were not visible with fluoroscopy neither with radiograph. In comparison to the seed‐migration detector, detection based on fluoroscopy would have led to four false‐negative detections (out of 33 or 12.1%) while the radiograph would have resulted in thirteen or 39.4%. Moreover, x‐ray would have required extra radiation dose to lung to 100% of the patients rather than the 21.3% who needed it in this study. The recommendation to perform chest radiographs should be revised because of superior efficacy of the seed‐migration detector. X‐rays should only remain for documentation purposes. Finally, the detector is convenient, cost‐effective and non‐invasive: it does not require any additional radiation to the patient.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".