Additional file 1 of Translation of PET radiotracers for cancer imaging: recommendations from the National Cancer Imaging Translational Accelerator (NCITA) consensus meeting
Bibliographic record
Abstract
Additional file 1: Tables S1–S8. Table S1. NCITA consensus panel members. Table S2. Detailed results for preclinical survey questions on existing radiotracers, novel radiotracers and stakeholder involvement. Table S3. Detailed results for clinical survey questions on existing radiotracers, novel radiotracers and stakeholder involvement. Table S4. First-round survey results (prior to the consensus meeting) for questions on preclinical evaluation methodologies for established and novel radiotracers and stakeholder involvement in the preclinical phase. Table S5. First-round survey results (prior to the consensus meeting) for questions on clinical validation methodologies for established and novel radiotracers and stakeholder involvement in the clinical phase. Table S6. Free-text comments from consensus panel members on preclinical validation requirements for existing or novel radiotracers. Table S7. Free-text comments from consensus panel members on clinical validation requirements for existing or novel radiotracers. Table S8. Free-text comments from consensus panel members on stakeholder involvement in preclinical and clinical radiotracer studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.843 | 0.255 |
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".