Structured reporting of neuroendocrine tumors in PET/CT using [18F]SiTATE - impact on interdisciplinary communication
Bibliographic record
Abstract
Abstract Our retrospective single-center study aims to evaluate the impact of structured reporting (SR) using a self-developed template on report quality compared to free-text reporting (FTR) in [ 18 F]SiTATE Positron Emission Tomography/Computer Tomography (PET/CT) for the primary staging and therapy monitoring of patients diagnosed with neuroendocrine tumors (NET). In total 50 patients were included. FTRs and SRs were generated post-examination. All reports were evaluated by a radiologist and a surgeon through a questionnaire to determine their contribution to facilitating clinical decision-making and to assess their completeness, linguistic quality, and overall quality. SR significantly increased the capacity of facilitating therapy decision-making from 32% in FTR to 55% in SR ( p < 0.001). Trust in the report was significantly higher in SR with a mean of 5.0 (SD = 0.5) vs. 4.7 (SD = 0.5) for FTR ( p < 0.001). SR received significantly higher mean ratings regarding linguistic quality with 4.7 for SR vs. 4.4 for FTR ( p = 0.004) and overall report quality with a mean of 4.9 for SR vs. 4.6 for FTR ( p < 0.001). Concluding that SR enhances the overall quality of reports in [ 18 F]SiTATE-PET/CTs for NET staging, serving as a tool to streamline clinical decision-making and enhance interdisciplinary communication in the future.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".