Bibliographic record
Abstract
Purpose Current guidelines for sincalide-stimulated cholescintigraphy (SSC) call for a 60-minute sincalide infusion, and a gallbladder ejection fraction (GBEF) ≥38% is considered normal. In this retrospective study, we hypothesize that most patients reach a normal GBEF by 30 minutes. Methods Eligible patients had undergone a 60-minute SSC from January to December 2019. The clinical SSC data were previously processed on a Xeleris workstation (GE Healthcare). In subjects with GBEF ≥38% based on standard SSC, the GBEF at 20 minutes and 30 minutes were retrospectively calculated using manual pixel height measurements. Receiving operating characteristic was analyzed to determine the best GBEF cutoff at 30 minutes. Results Of 302 subjects, mean age of 46 ± 17 years, 33 (10.9%) showed an abnormal GBEF <38% suggestive of functional gallbladder disorder. In the remaining 269 patients (89.1%) with a normal GBEF, 60.6% and 86.6% reached a normal GBEF at 20 minutes and 30 minutes, respectively. Moreover, a GBEF threshold >29.1% at 30 minutes was associated with a negative predictive value of 99.6%, indicating that a 60-minute SSC was not necessary. The GBEF values were not associated with sex, age, patient symptoms, or type of referral. Manually calculated GBEFs on the time-activity curve showed excellent correlation with the primary values. We propose a modified workflow that splits the 60-minute SSC into two 30-minute image sets to allow for a screening GBEF at 30 minutes. If GBEF is >29.1% at 30 minutes, the second image set may be stopped, and the examination is complete. Conclusions The majority of patients (77.2%) undergoing the standard 60-minute SSC reach a normal GBEF already by 30 minutes. The proposed workflow shortens the SCC procedure by 30 minutes, while maintaining high diagnostic accuracy and contributing to improved procedure efficiency and reduced patient discomfort as well as symptoms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".