PET and PET/CT Reports: Observations from the National Oncologic PET Registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it