Practical aspects of PET oncology imaging while receiving 18F-FDG from a distant supplier
Bibliographic record
Abstract
2126 Objectives This work discusses challenges faced by a PET/CT centre receiving 18F-FDG from a distant supplier. The secondary objective is to describe contingency plans for optimizing received shipments. Methods For over 3 years our facility has been receiving 18F-FDG transported by commercial airline from a supplier over 1000km away. In this time we have developed contingency plans for managing shipments along the transport route and an operational model incorporating maximum flexibility. Key to increasing patient scans are:1.Careful pre-screening of patients prior to their scan date avoids on-site cancellations. Patient questions are also addressed in order to enhance efficiency on scanning day.2.Timely communication of shipped activity to multiple email recipients every morning. Expected activity and arrival time are entered into a spreadsheet to quickly determine if all scheduled patients can be scanned or if low activity contingency plans must be used.3.Good communication with supplier, courier, and airline personnel, among others, aids in timely delivery of our 18F-FDG through awareness of the impact of delays on patient care. Results Table 1 shows the scan totals for the past 3 years. We have been able to increase the number of scans by 51% despite only increasing the number of scanning days by 24% while observing a marked increase in supplier production problems. Conclusions When receiving 18F-FDG from a distant vendor significant cancellations can be anticipated due to production and transport problems. Through careful establishment of contingency protocols, disruptions to patient care can be minimized.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".