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Record W2943251302 · doi:10.1002/cam4.4058

Impact of the model of long‐term follow‐up care on adherence to guideline‐recommended surveillance among survivors of adolescent and young adult cancers

2021· article· en· W2943251302 on OpenAlexafffundabout
Dalia Kagramanov, Rinku Sutradhar, Cindy Lau, Zhan Yao, Jason D. Pole, Nancy N. Baxter, Sumit Gupta, Paul C. Nathan

Bibliographic record

VenueCancer Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsInstitute for Clinical Evaluative SciencesSt. Michael's HospitalMount Sinai HospitalHospital for Sick ChildrenToronto Rehabilitation InstituteUniversity of Toronto
FundersOntario Ministry of Health and Long-Term CareCancer Care OntarioPediatric Oncology Group of OntarioC17 CouncilCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchAlex's Lemonade Stand Foundation for Childhood Cancer
KeywordsGuidelineTerm (time)MedicinePediatricsGerontologyIntensive care medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: Adolescent and young adult cancer survivors require lifelong healthcare to address the late effects of therapy. We examined the impact of different provider models of long-term follow-up (LTFU) care on adherence to recommended surveillance. METHODS: We conducted a retrospective cohort study using administrative health databases in Ontario, Canada. Five-year survivors were identified from IMPACT, a database of patients aged 15-20.9 years at diagnosis of six cancers between 1992 and 2010. We defined three models of LTFU care hierarchically: specialized survivor clinics (SCCs), general cancer clinics (GCCs), and family physician (FP). We assessed adherence to the Children's Oncology Group surveillance guidelines for cardiomyopathy and breast cancer. Multistate models assessed adherence transitions and impacts of LTFU attendance. RESULTS: A total of 1574 survivors were followed for a mean of 9.2 years (range 4.3-13.9 years) from index (5-year survival). The highest level of LTFU attended in the first 2-years post-index was a GCC (47%); only 16.7% attended a SCC. By the end of study, 72% no longer attended any of the models of care and only 2% still attended an SCC. Among 188 survivors requiring breast cancer surveillance, 6.9% were adherent to their first required surveillance testing. Attendance at a SCC in the previous year and higher cumulative FP or GCC visits increased the rate of subsequently becoming adherent. Among 857 survivors requiring cardiomyopathy surveillance, 11% were adherent at study entry. Each subsequent SCC visit led to an 11.3% (95% CI: 1.05-1.18) increase in the rate of becoming adherent. CONCLUSION: LTFU attendance and surveillance adherence are sub-optimal. SCC follow-up is associated with greater adherence, but few survivors receive such care, and this proportion diminished over time. Interventions are needed to improve LTFU attendance and promote surveillance adherence.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.510
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.044
GPT teacher head0.364
Teacher spread0.319 · 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

Citations40
Published2021
Admission routes3
Has abstractyes

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