Intra- and inter-radiation therapist reproducibility of daily isocenter verification using prostatic fiducial markers
Bibliographic record
Abstract
BACKGROUND: We sought to determine the intra- and inter-radiation therapist reproducibility of a previously established matching technique for daily verification and correction of isocenter position relative to intraprostatic fiducial markers (FM). MATERIALS AND METHODS: With the patient in the treatment position, anterior-posterior and left lateral electronic images are acquired on an amorphous silicon flat panel electronic portal imaging device. After each portal image is acquired, the therapist manually translates and aligns the fiducial markers in the image to the marker contours on the digitally reconstructed radiograph. The distances between the planned and actual isocenter location is displayed. In order to determine the reproducibility of this technique, four therapists repeated and recorded this operation two separate times on 20 previously acquired portal image datasets from two patients. The data were analyzed to obtain the mean variability in the distances measured between and within observers. RESULTS: The mean and median intra-observer variability ranged from 0.4 to 0.7 mm and 0.3 to 0.6 mm respectively with a standard deviation of 0.4 to 1.0 mm. Inter-observer results were similar with a mean variability of 0.9 mm, a median of 0.6 mm, and a standard deviation of 0.7 mm. When using a 5 mm threshold, only 0.5% of treatments will undergo a table shift due to intra or inter-observer error, increasing to an error rate of 2.4% if this threshold were reduced to 3 mm. CONCLUSION: We have found high reproducibility with a previously established method for daily verification and correction of isocenter position relative to prostatic fiducial markers using electronic portal imaging.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".