Travel Distance and Its Impact on Wait Time for Positron Emission Tomography–Computed Tomography in Patients with Cancers
Bibliographic record
Abstract
Objectives: To examine travel distance and its impact on wait time for Positron emission tomography–computed tomography (PET/CT) in patients with lung, lymphoma, and prostate cancers in Alberta. Methods: We used Alberta cancer registry and diagnostic imaging database to identify patients with lung, lymphoma, and prostate cancers who had a PET/CT scan during April 2017 and March 2023. Alberta Facilities Distance/Time Look Up Table was used to calculate travel distance from patient’s residence to PET/CT facility. Negative binomial regression was used to assess association between travel distance and wait time for PET/CT. Results: The study included 9,503 patients. Lung cancer accounted for 43.4% of patients, followed by lymphoma (37.1%) and prostate (19.5%) cancers. There were more female patients with lung (55.5%) than lymphoma (42.9%; p< 0.001) cancers. Mean (SD) age was 66.8 (13.8) years and lymphoma patients were younger (59.6 years) than lung (70.3 years; p< 0.001) or prostate (72.7 years; p< 0.001) patients. Diabetes (14.2%) was the most prevalent comorbidity. Median (IQR) travel distance was 21 (12-121) km and was shorter for urban (16 km) than rural (148 km; p< 0.001) patients, but wait time was similar (median=20 vs. 21 days; p=0.378). There were no significant associations between travel distance and wait time (IRR=1.00; p=0.108). The results were robust in subgroup analyses by type of cancer and scan priority. Conclusion: There were no associations between travel distance and wait time for PET/CT. Additional research is warranted to examine the potential impact of longer travel distance on overall access to care and patient outcomes.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".