Contextualizing the Use of Oncologic Imaging within Treatment Phases: Imaging Trends and Modality Preferences, 2000–2014
Bibliographic record
Abstract
Background: In the present study, we retrospectively evaluated the use of tomographic imaging in adult cancer patients to clarify how recent growth plateaus in the use of tomographic imaging in the United States might have affected oncologic imaging during the same period. Methods: At a U.S. academic cancer centre, 12,059 patients with dates of death from January 2000 through December 2014 were identified. Imaging was restricted to brain and body computed tomography (ct), brain and body magnetic resonance (mr), and body positron-emission tomography (pet) with and without superimposed ct. Trends during the staging (1 year after diagnosis), monitoring (18–6 months before death), and end-of-life (final 6 months before death) phases were analyzed. Results: Comparing the 2005–2009 with the 2010–2014 period, mean intensity of pet imaging increased 21% during staging (p = 0.0000) and 27% during end of life (p = 0.0019). In the monitoring phase, mean intensity for ct brain, ct body, and mr body imaging decreased by 26% (p = 0.0133), 11% (p = 0.0118), and 26% (p = 0.0008), respectively. Aggregate mean intensity of imaging increased in the 13%–27% range every 3 months from 18 months before death to death, reaching 1.43 images in the final 3 months of life. Patients diagnosed in the final 18 months of life had an average of 1 additional image during both the 3 months after diagnosis (p = 0.0000) and the final 3 months before death (p = 0.0000). Conclusions: Imaging increased as temporal proximity to death decreased, and patients diagnosed near death received more staging imaging, suggesting that imaging guidelines should consider imaging intensity within the context of treatment phase. Despite the development, by multiple organizations, of appropriateness criteria to reduce imaging utilization, aggregate per-patient imaging showed insignificant changes. Simultaneous fluctuations in the intensity of imaging by modality suggest recent changes in the modalities preferred by providers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".