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Record W4327544451 · doi:10.1155/2023/5056408

Variations in Emergency Service Utilization among Cancer Survivors: Results from the Pan-Canadian Experiences of Cancer Patients in Transition Study Survey

2023· article· en· W4327544451 on OpenAlexafffundabout
Megan Delisle, Ying Wang, Margaret I. Fitch, Kalki Nagaratnam, Amirrtha Srikanthan

Bibliographic record

VenueJournal of Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaOttawa HospitalUniversity of Ottawa
FundersPartenariat Canadien Contre Le Cancer
KeywordsMedicineCancerSurvivorship curveProstate cancerLogistic regressionQuality of life (healthcare)Multinomial logistic regressionFamily medicineColorectal cancerBreast cancerGerontologyInternal medicineNursing

Abstract

fetched live from OpenAlex

Purpose: The objective of this study was to examine variations in emergency service utilization (ESU) among cancer survivors during the first year after completing primary cancer treatment. Methods: In 2016, the Canadian Partnership Against Cancer collected survey responses from cancer survivors across Canada about self-reported ESU after completing primary cancer treatment. We included survey respondents diagnosed with nonmetastatic breast, hematologic, colorectal, melanoma, or prostate cancer. Multivariable, multinomial logistic regression analysis was used to examine factors associated with cancer survivors' ESU. Results: Of the 5,774 cancer survivors included in our analysis, 22% reported ESU during the first year after completing their primary cancer treatment, 16% reported ESU one to three times, and 6% reported ESU more than three times. Factors significantly associated with frequent ESU included younger age, colorectal and hematologic cancers, more frequent primary care provider and oncology specialist visits, single or retired status, lower income, and self-reported lower quality of life. Conclusion: Our study identified factors associated with more frequent ESU among cancer survivors in the first year after completing primary cancer treatment. These factors highlight differences in cancer survivors' demographics, their ability to access and need for healthcare services, and the complexity of using ESU as a metric for quality improvement in survivorship care. These variations must be considered in quality improvement initiatives.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.127
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.372
Teacher spread0.301 · 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 teacher head, 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

Citations0
Published2023
Admission routes3
Has abstractyes

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