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Primary care utilization and cardiovascular screening in adult-aged survivors of childhood cancer: A report from the childhood cancer survivor study (CCSS).

2023· article· en· W4379341829 on OpenAlexaff
Timothy J. D. Ohlsen, Yan Chen, Laura‐Mae Baldwin, Melissa M. Hudson, Paul C. Nathan, Claire Snyder, Karen L. Syrjala, Emily S. Tonorezos, Yutaka Yasui, Gregory T. Armstrong, Kevin C. Oeffinger, Eric J. Chow

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenUniversity of Alberta
FundersNational Institutes of Health
KeywordsMedicineCancerDiabetes mellitusMedical recordPediatric cancerInternal medicinePediatricsPhysical therapy

Abstract

fetched live from OpenAlex

e22020 Background: Among the half million survivors of childhood cancer in the US, cardiovascular disease (CVD) is the leading non-cancer cause of premature death. Adult survivors of childhood cancer are largely managed by community-based primary care providers (PCPs), and healthcare utilization patterns related to CVD screening and survivorship care are not well-described. Methods: Within the CCSS cohort, we conducted a randomized intervention trial (NCT03104543) focused on improving CVD risk factor control among survivors at increased risk for CVD due to prior chest radiotherapy and/or anthracycline chemotherapy. Using medical records from participants’ PCPs over the 2 years preceding trial enrollment, we ascertained the numbers of PCP and specialist visits, CVD conditions (i.e., hypertension, dyslipidemia, diabetes) and screening (i.e., blood pressure, lipid, diabetes testing, and cardiac testing [ECG, echocardiogram, or other imaging completed or planned]). We also abstracted acknowledgement of participants’ cancer history, cardiotoxic treatment exposures, or a survivorship care plan (SCP). Multivariable logistic regression assessed characteristics associated with having cardiac testing, retaining covariates associated with p < 0.10 in univariate testing. Results: Of 347 enrolled participants, 293 (84%) had evaluable data (median age 40y, range 22–65; 49% female; 87% non-Hispanic White; mean 31y since childhood cancer). In the prior 2y, 81% of participants had a documented PCP office visit (median 3 visits [IQR 2–5]), 22% had a subspecialty visit (4% saw cardiology), and 16% had no visits. The prevalence of blood pressure, lipid, and diabetes screening was 82%, 57%, and 63%, respectively; 29% had cardiac testing done or planned, including 22% with echocardiography. Only 68% of participants had records referencing a history of cancer. PCP documentation of prior cardiotoxic exposures was low compared with known exposures: radiotherapy (35% vs 69%; p < 0.001), anthracycline chemotherapy (9% vs 76%; p = 0.017); only 12% had any documentation noting an increased risk for CVD. Few participants’ records referenced a need for cancer-related late effects surveillance (38%), and even fewer referenced an SCP (5%). In multivariable analysis, independent predictors of cardiac testing included documentation of increased CVD risk (Odds Ratio [OR] 11.61, 95% CI 3.31–40.67), presence of a late effects surveillance plan (OR 3.71, 95% CI 1.62–8.48), and existing CVD conditions (modeled as 0, 1, or 2+ conditions; OR 2.21, 95% CI 1.41–3.47, for each additional level). Conclusions: Adult survivors of childhood cancer at increased risk of CVD had low rates of cardiac testing and documentation of risk in PCP medical records. Increasing participant and PCP awareness of CVD risks and late effects surveillance recommendations may improve screening.

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)0.003
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.127
GPT teacher head0.442
Teacher spread0.315 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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