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Record W4317214822 · doi:10.2196/40725

Web-Based Asynchronous Tool to Facilitate Communication Between Primary Care Providers and Cancer Specialists: Pragmatic Randomized Controlled Trial

2023· article· en· W4317214822 on OpenAlexafffund
Bojana Petrovic, Jim A. Julian, Clare Liddy, Amir Afkham, Sharon F. McGee, Scott C. Morgan, Roanne Segal, Jonathan Sussman, Gregory R. Pond, Mary Ann O’Brien, Jacqueline L. Bender, Eva Grunfeld

Bibliographic record

VenueJournal of Medical Internet Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreOttawa HospitalOntario Institute for Cancer ResearchBruyèreUniversity of OttawaMcMaster UniversityPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineSurvivorship curveRandomized controlled trialBreast cancerRandomizationCancerFamily medicineHealth carePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer poses a significant global health burden. With advances in screening and treatment, there are now a growing number of cancer survivors with complex needs, requiring the involvement of multiple health care providers. Previous studies have identified problems related to communication and care coordination between primary care providers (PCPs) and cancer specialists. OBJECTIVE: This study aimed to examine whether a web- and text-based asynchronous system (eOncoNote) could facilitate communication between PCPs and cancer specialists (oncologists and oncology nurses) to improve patient-reported continuity of care among patients receiving treatment or posttreatment survivorship care. METHODS: In this pragmatic randomized controlled trial, a total of 173 patients were randomly assigned to either the intervention group (eOncoNote plus usual methods of communication between PCPs and cancer specialists) or a control group (usual communication only), including 104 (60.1%) patients in the survivorship phase (breast and colorectal cancer) and 69 (39.9%) patients in the treatment phase (breast and prostate cancer). The primary outcome was patient-reported team and cross-boundary continuity (Nijmegen Continuity Questionnaire). Secondary outcome measures included the Generalized Anxiety Disorder Screener (GAD-7), Patient Health Questionnaire on Major Depression, and Picker Patient Experience Questionnaire. Patients completed the questionnaires at baseline and at 2 points following randomization. Patients in the treatment phase completed follow-up questionnaires at 1 month and at either 4 months (patients with prostate cancer) or 6 months following randomization (patients with breast cancer). Patients in the survivorship phase completed follow-up questionnaires at 6 months and at 12 months following randomization. RESULTS: The results did not show an intervention effect on the primary outcome of team and cross-boundary continuity of care or on the secondary outcomes of depression and patient experience with their health care. However, there was an intervention effect on anxiety. In the treatment phase, there was a statistically significant difference in the change score from baseline to the 1-month follow-up for GAD-7 (mean difference -2.3; P=.03). In the survivorship phase, there was a statistically significant difference in the change score for GAD-7 between baseline and the 6-month follow-up (mean difference -1.7; P=.03) and between baseline and the 12-month follow-up (mean difference -2.4; P=.004). CONCLUSIONS: PCPs' and cancer specialists' access to eOncoNote is not significantly associated with patient-reported continuity of care. However, PCPs' and cancer specialists' access to the eOncoNote intervention may be a factor in reducing patient anxiety. TRIAL REGISTRATION: ClinicalTrials.gov NCT03333785; https://clinicaltrials.gov/ct2/show/NCT03333785.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.065
GPT teacher head0.401
Teacher spread0.336 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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