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Symptom severity, specialty palliative care, and subsequent symptom control among adolescents and young adults with cancer: A population-based study.

2023· article· en· W4379282499 on OpenAlexafffundabout
Natalie G. Coburn, Sumit Gupta, Li Q, Alisha Kassam, Adam Rapoport, Kimberley Widger, Karine Chalifour, Nancy N. Baxter, Paul C. Nathan, Rinku Sutradhar

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSouthlake Regional Health CenterHospital for Sick ChildrenHealth Sciences CentreSunnybrook Health Science Centre
FundersTerry Fox Research Institute
KeywordsMedicineSpecialtyCancerPalliative carePopulationCohortYoung adultDiseaseInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

e24127 Background: Adolescents and young adults (AYA) with cancer can suffer from substantial symptom burdens. Specialty palliative care (SPC) is recommended but often uninvolved or involved late. In a population-based AYA cohort, we determined: 1) whether symptom severity measured by routine patient report was associated with subsequent SPC involvement; and 2) whether SPC involvement was associated with subsequent symptom improvement. Methods: All Ontario, Canada AYA diagnosed with cancer aged 15-29 between 2010-2018 were identified and linked to healthcare databases, including one capturing self-reported Edmonton Symptom Assessment System (ESAS) scores at cancer-related visits. SPC was identified through validated algorithms using PC fee codes billed by SPC physicians. ESAS scores were categorized as not measured, mild (0-3), moderate (4-6), or severe (7-9), and could vary over time. For each symptom, the association of ESAS score with subsequent SPC involvement was determined, adjusting for patient and disease characteristics. For objective #2, only AYA who died within 5 years of cancer diagnosis were included and a “difference-in-difference” approach was used. Cases (SPC involvement before death, index date was time of first SPC service) were matched 1:1 to controls (no SPC involvement at equivalent index date, defined by time prior to death) by sex and cancer type. By symptom type, linear regression determined whether the difference between the 90-day post-index and 90-day pre-index mean ESAS scores was itself different between cases and controls through examining interaction terms. Results: 5,435 AYA met inclusion criteria. 5-year cumulative incidence of SPC involvement was 19% [95th confidence interval (95CI) 18-20%]. For all symptoms, moderate and severe scores were associated with increasing likelihood of SPC involvement compared to mild scores; not being ESAS screened was associated with decreased likelihood. The greatest magnitude of association was seen for pain scores [adjusted hazard ratio of SPC involvement for severe vs. mild 7.7, 95CI 5.8-10.2, p < 0.001]. 721 (13.3%) AYA died within 5 years of diagnosis; 612 (84.9%) had at least one SPC visit prior to death. 202 case-control pairs were identified. SPC involvement was associated with improved pain score trajectories (mean pain scores improved from 3.4 to 3.1 in cases vs. worsened from 1.9 to 2.1 in controls; p = 0.003), but did not impact trajectories of other symptoms. Conclusions: AYA reporting moderate or severe symptoms through a provincial screening program were more likely to subsequently receive SPC; systematic screening may increase access. SPC was associated with a subsequent decrease in pain severity, but did not affect other symptoms. New interventions targeting these other symptoms during cancer treatment and particularly at the end-of-life are urgently needed.

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)0.002
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 score0.116
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.056
GPT teacher head0.422
Teacher spread0.367 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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