Poster - 03: How to manage a nuclear medicine PET-CT for radiation oncology patients
Bibliographic record
Abstract
Purpose: Development of an adapted multidisciplinary procedure designed to optimize the clinical workflow between radiation therapy (RT) and nuclear medicine (NM) for a PET-CT located in the NM department. Methods : The radiation oncologist (RO) prescribes the PET-CT exam and the clinical RT therapist gives all the necessary information to the patient prior to the exam. The immobilization accessories are prepared in the RT department. The RT and NM therapists work together for radiotracer injection, patient positioning and scan acquisition. The nuclear medicine physician (NMP) will study the images, draw Biological Target Volumes (BTVs) and produce a full exam report. Results : All tasks related to a planning PET-CT are done within 48 hours from the request by the RO to the reception of the images with the NMP contours and report. Conclusions : By developing a complete procedure collectively between the RT and NM departments, the patient benefits of a quick access to a RT planning PET-CT exam including the active involvement of every medical practitioners in these fields.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.031 |
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".