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Communicating cardiovascular health information and improving coordination with primary care: A Childhood Cancer Survivor Study randomized trial.

2024· article· en· W4399304545 on OpenAlexaff
Eric J. Chow, Yan Chen, Yutaka Yasui, Laura‐Mae Baldwin, Melissa M. Hudson, Tammy Muller, Paul C. Nathan, Siu L. Ngai, Timothy J. D. Ohlsen, Claire Snyder, Karen L. Syrjala, Emily S. Tonorezos, Gregory T. Armstrong, Kevin C. Oeffinger

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick Children
FundersNational Institutes of Health
KeywordsMedicineRandomized controlled trialChildhood cancerCancerPrimary careFamily medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

10011 Background: Childhood cancer survivors are at risk of early cardiovascular disease (CVD). We conducted a survivorship care plan (SCP)-based counseling intervention to improve CVD risk factor control in adult-aged survivors. Methods: Randomized (1:1) trial of survivors at high CVD risk based on history of anthracycline or chest radiotherapy exposures with undertreated hypertension (≥130/80 mmHg), dyslipidemia (LDL ≥160 mg/dL or triglyceride ≥200 mg/dL), and/or glucose intolerance (threshold varied if history of pre-diabetes or diabetes) based on in-home testing. Approximating a survivorship clinic visit, the intervention consisted of a remotely delivered session with an advanced practice provider to review results, a SCP with personalized CVD risk information, and an action plan to help manage CVD risk factors. A remote booster session was provided 4 months later with the action plan updated. Control participants only received a copy of their in-home results with abnormalities noted and written encouragement to follow-up with their primary care provider (PCP). Blood pressure, lipid profile, and glucose tolerance were retested after 1y. For both groups, all participant materials were sent to PCPs throughout the study, and PCP medical records were abstracted at study completion. Logistic regression assessed the odds ratio (OR) for undertreatment at 1y associated with the intervention, adjusting for pre-specified variables (sex, current age, time since cancer, insurance status, recent history of survivorship clinic visit, and undertreated CVD risk factor). Results: Among 644 survivors who completed in-home testing, 347 met inclusion criteria and were randomized (175 intervention; overall 52% male, mean age 40y, 31y since cancer diagnosis); 264 with 1y follow-up (126 intervention). At baseline, rates of hypertension, dyslipidemia, and glucose intolerance were 53%, 52%, and 49%, respectively; 43% had > 1 undertreated condition. Although the intervention achieved > 95% satisfaction, it was not associated with reduced undertreatment vs control (OR 0.9, 95% CI 0.7-1.3). Notably, 48% of intervention and 44% of control participants had less undertreatment after 1y. In secondary analysis, greater internal locus of control was associated with less undertreatment at 1y (OR 0.7, 95% CI 0.6-0.9). The intervention group was more likely than controls to have CVD risk (+10 vs -1%), SCP (+17 vs -2%), and some late effects surveillance plan (+8 vs +2%) documented within PCP records at 1y vs baseline (p < 0.05 for all). Conclusions: While a remotely delivered counseling intervention did not reduce CVD risk factor undertreatment compared with provision of test results alone, both study arms had > 40% reduction in undertreatment. These results suggest that simply providing a formal CVD risk assessment to high-risk cancer survivors and their PCPs may be effective. Clinical trial information: NCT03104543 .

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.063
GPT teacher head0.434
Teacher spread0.371 · 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 designRandomized trial
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
Published2024
Admission routes1
Has abstractyes

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