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Association between high-cost imaging and hospice use at the end of life of cancer patients.

2013· article· en· W2970537945 on OpenAlexaff
Michaela A. Dinan, Soko Setoguchi, Lesley H. Curtis, Winson Y. Cheung

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineQuartileLung cancerCancerProstate cancerLogistic regressionColorectal cancerEnd-of-life careBreast cancerInternal medicineRadiologyOncologyPalliative careConfidence interval

Abstract

fetched live from OpenAlex

6504 Background: Use of high cost imaging modalities can inform prognosis and potentially de-escalate unnecessary care at the end of life of cancer patients, but it may also be associated with increased health care expenditures. This analysis investigated the association of hospital-level use of advanced imaging studies with aggressiveness of care near death, as measured by hospice enrollment. Methods: Using SEER-Medicare, patients who died from common solid tumors between 2002 and 2007 were identified and hospital-level utilization of CT, MRI and PET scans were categorized into quartiles. Patients were assigned to the hospital at which they spent the majority of their inpatient days during the last 6 months of life. Multivariate-adjusted logistic regression models based on tumor type were constructed to correlate imaging use with late as well as all hospice admissions, defined as enrollment within 3 days and 18 months of death, respectively. Results: A total of 7,876 breast, 31,933 lung, 12,877 colorectal and 9,137 prostate cancer patients were included. High cost imaging varied across tumor types with rates being highest for lung cancer (mean of 2.29 CT, 0.58 MRI and 0.21 PET scans per patient) and lowest for prostate cancer (mean of 1.25 CT, 0.41 MRI, and 0.01 PET scans per patient) in the 6 months before death. Overall hospice use was least frequent in colorectal cancer (50% of patients) while late hospice admission was most common in lung cancer (11% of patients). Hospitals within the top quartile of imaging use generally had decreased odds of hospice utilization and higher likelihood of late hospice enrollment across most cancer types (Table). Conclusions: In this large cohort, receipt of care near end of life in hospitals with the highest rates of advanced imaging use was associated with lower use of and later enrollment to hospice. High cost imaging is not correlated with de-escalation of aggressive care. [Table: see text]

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.446
Teacher spread0.359 · 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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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