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Record W3128679106 · doi:10.2196/18269

Comparative Analysis of Paper-Based and Web-Based Versions of the National Comprehensive Cancer Network-Functional Assessment of Cancer Therapy-Breast Cancer Symptom Index (NFBSI-16) Questionnaire in Breast Cancer Patients: Randomized Crossover Study

2021· article· en· W3128679106 on OpenAlexvenueno aff
Jinfei Ma, Zihao Zou, Emmanuel Eric Pazo, Salissou Moutari, Ye Liu, Feng Jin

Bibliographic record

VenueJMIR Medical Informatics · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBreast cancerMedicineConcordancePhysical therapyCancerRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer remains the most common neoplasm diagnosed among women in China and globally. Health-related questionnaire assessments in research and clinical oncology settings have gained prominence. The National Comprehensive Cancer Network-Functional Assessment of Cancer Therapy-Breast Cancer Symptom Index (NFBSI-16) is a rapid and powerful tool to help evaluate disease- or treatment-related symptoms, both physical and emotional, in patients with breast cancer for clinical and research purposes. Prevalence of individual smartphones provides a potential web-based approach to administrating the questionnaire; however, the reliability of the NFBSI-16 in electronic format has not been assessed. OBJECTIVE: This study aimed to assess the reliability of a web-based NFBSI-16 questionnaire in breast cancer patients undergoing systematic treatment with a prospective open-label randomized crossover study design. METHODS: We recruited random patients with breast cancer under systematic treatment from the central hospital registry to complete both paper- and web-based versions of the questionnaires. Both versions of the questionnaires were self-assessed. Patients were randomly assigned to group A (paper-based first and web-based second) or group B (web-based first and paper-based second). A total of 354 patients were included in the analysis (group A: n=177, group B: n=177). Descriptive sociodemographic characteristics, reliability and agreement rates for single items, subscales, and total score were analyzed using the Wilcoxon test. The Lin concordance correlation coefficient (CCC) and Spearman and Kendall τ rank correlations were used to assess test-retest reliability. RESULTS: Test-retest reliability measured with CCCs was 0.94 for the total NFBSI-16 score. Significant correlations (Spearman ρ) were documented for all 4 subscales-Disease-Related Symptoms Subscale-Physical (ρ=0.93), Disease-Related Symptoms Subscale-Emotional (ρ=0.85), Treatment Side Effects Subscale (ρ=0.95), and Function and Well-Being Subscale (ρ=0.91)-and total NFBSI-16 score (ρ=0.94). Mean differences of the test and retest were all close to zero (≤0.06). The parallel test-retest reliability of subscales with the Wilcoxon test comparing individual items found GP3 (item 5) to be significantly different (P=.02). A majority of the participants in this study (255/354, 72.0%) preferred the web-based over the paper-based version. CONCLUSIONS: The web-based version of the NFBSI-16 questionnaire is an excellent tool for monitoring individual breast cancer patients under treatment, with the majority of participants preferring it over the paper-based version.

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)0.010
metaresearch head score (Gemma)0.015
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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.346
Teacher spread0.329 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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