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Symptom Screening for Hospitalized Pediatric Patients With Cancer

2024· article· en· W4404319358 on OpenAlexaffabout
L. Lee Dupuis, Donna L. Johnston, David Dix, Sarah McKillop, Sadie Cook, Nicole Crellin‐Parsons, Ketan Kulkarni, Serina Patel, Magimairajan Vanan, Paul Gibson, Dilip Soman, Susan Kuczynski, George Tomlinson, Lillian Sung

Bibliographic record

VenueJAMA Pediatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsToronto General HospitalCanadian Partnership Against CancerHamilton Health SciencesUniversity of ManitobaCancerCare ManitobaMcMaster Children's HospitalLondon Health Sciences CentreStollery Children's HospitalIzaak Walton Killam Health CentreUniversity of AlbertaChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesBC Children's HospitalUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicinePediatric cancerCancerPediatric oncologyPediatricsMEDLINEIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Importance: Pediatric patients with cancer experience severely bothersome symptoms during treatment. It was hypothesized that symptom screening and provision of symptom reports to the health care team would reduce symptom burden in pediatric patients with cancer. Objective: To determine if daily symptom screening and provision of symptom reports to the health care team was associated with lower total symptom burden as measured by the Symptom Screening in Pediatrics Tool (SSPedi) compared to usual care among pediatric patients with cancer admitted to a hospital or seen in a clinic daily for at least 5 days. Design, Setting, and Participants: This randomized clinical trial enrolled participants from July 2018 to September 2023 from 8 Canadian tertiary care centers that diagnose and treat pediatric patients with cancer. Patients aged 8 to 18 years with cancer expected to be in a hospital or clinic daily for at least 5 consecutive days were eligible for inclusion. Participants were randomized to intervention (n = 176) vs control (n = 169) groups. Data were analyzed from November 2023 to December 2023. Intervention: Intervention participants completed the SSPedi once daily for 5 days. Printed symptom reports were provided daily to the health care team, and email alerts were distributed for severely bothersome symptoms. Control participants received usual care. Main Outcomes and Measures: The primary outcome was self-reported total SSPedi score on day 5. Secondary outcomes were individual SSPedi symptoms, pain, quality of life, symptom documentation, and intervention provision. The primary analysis compared the day 5 total SSPedi scores between randomized groups using a multiple linear regression model. For the secondary analysis comparing individual SSPedi symptom scores, the odds ratio for the intervention was estimated using a proportional odds model. Pain and quality of life were analyzed using the same approach as the primary outcome. Fisher exact test was used to compare symptom documentation, any intervention, and symptom-specific intervention between groups. Results: A total of 345 participants were enrolled; median (range) participant age was 13.8 (8.0-18.8) years, and 150 participants (43.5%) were female. Day 5 SSPedi score was significantly better with symptom screening compared to usual care (adjusted mean difference, -2.5; 95% CI, -3.8 to -1.2). Symptom screening reduced the odds of higher individual symptom scores; 8 of 15 symptom reductions were statistically significant. There were no significant differences in pain or quality of life scores between groups. Five symptoms were documented or treated significantly more often with symptom screening than usual care. Conclusions and Relevance: In this randomized clinical trial, among pediatric patients with cancer admitted to a hospital or seen in a clinic daily for at least 5 days, symptom screening with SSPedi improved total symptom scores compared to usual care. Trial Registration: ClinicalTrials.gov Identifier: NCT03593525.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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.

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

Citations23
Published2024
Admission routes2
Has abstractyes

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