PET and PET/CT Reports: Observations from the National Oncologic PET Registry
Bibliographic record
Abstract
UNLABELLED: Our objective was to identify core elements for inclusion in oncologic PET reports and to evaluate a sample of reports in the National Oncologic PET Registry database. METHODS: A list of desirable elements in PET reports was compiled from American College of Radiology and Society of Nuclear Medicine guidelines. A training set of 20 randomly selected reports was evaluated by the 4-physician panel, and the results were used to formulate a consensus approach for assessing report content and quality. Each reviewer then scored 65 randomly selected reports-20 common to all reviewers. The scores were tabulated, and interrater variability was measured for the common cases. RESULTS: Each report was assessed for 34 elements-21 primary and 11 additional questions related to 6 of these primary elements. Among the common cases, there was strong (> or = 0.70) interrater agreement for 30 of 34 elements. Among the unique cases, only 9 elements were included in more than 90% of the reports. Several important elements were not included in more than 40% of the reports: the reason for the study, a description of treatment history, a statement about comparison to other imaging, and time from radiopharmaceutical injection to imaging. CONCLUSION: Essential elements that should be included in oncologic PET reports were missing from many reports. These deficiencies may render the reports less helpful to referring physicians, may lead to misdiagnoses, and may cause coding and billing errors. Interpreting physicians should audit their reports to ascertain that they include appropriate elements necessary for billing compliance and for effective communications with referring physicians.
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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 | 0.023 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".