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Record W2610570045 · doi:10.3747/co.24.3216

Contextualizing the Use of Oncologic Imaging within Treatment Phases: Imaging Trends and Modality Preferences, 2000–2014

2017· article· en· W2610570045 on OpenAlexvenueno aff
Timothy P. Copeland, Jennifer M. Creasman, David Seidenwurm, Benjamin L. Franc

Bibliographic record

VenueCurrent Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsnot available
FundersHelen Diller Family Comprehensive Cancer Center, University of California, San Francisco
KeywordsMedicineMagnetic resonance imagingPositron emission tomographyNuclear medicineWhole body imagingNeuroimagingRadiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.365
GPT teacher head0.480
Teacher spread0.115 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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