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Record W4318600067 · doi:10.2196/42491

Development and Economic Evaluation of a Patient-Centered Care Model for Children With Duchenne Muscular Dystrophy: Protocol for a Quasi-Experimental Study

2023· article· en· W4318600067 on OpenAlexvenueno aff
Titiksha Sirari, Renu Suthar, Amarjeet Singh, Shankar Prinja, Vishwas Gupta, Manisha Malviya, Akashdeep Singh Chauhan, Naveen Sankhyan

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersPostgraduate Institute of Medical Education and Research, Chandigarh
KeywordsMedicineDuchenne muscular dystrophyIntervention (counseling)TelehealthPhysical therapyProtocol (science)TelemedicineQuality of life (healthcare)AmbulatoryHealth careFamily medicineNursingAlternative medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Duchenne muscular dystrophy (DMD) is a rare progressive muscular disease that primarily affects boys. A lack of comprehensive care for patients living with DMD is directly associated with a compromised quality of life (QoL) for those affected and their caregivers. This disease also has a huge economic impact on families as its treatment requires substantial direct, indirect, and informal care costs. OBJECTIVE: This study presents a protocol developed to evaluate the feasibility and efficacy of a patient-centered care (PCC) model for children with DMD. The care model was designed with the aim to empower families, improve QoL, and reduce economic burden on their families. METHODS: This study is planned as a quasi-experimental study that will enroll 70 consecutive families with boys (aged 5-15 years) with DMD visiting a tertiary care center. The study is being conducted in 2 phases (preintervention and postintervention phases, referred to as phase 1 and phase 2, respectively). During phase 1, the patients received routine care. The study is now in phase 2, with the intervention currently being administered. The intervention is based on the PCC model individualized by the intervention team. The model has a comprehensive DMD telecare component that includes teleconsultation as one of its key components to reduce in-person physician visits at the health facility. Teleconsultation is especially beneficial for late-ambulatory and nonambulatory patients. Data on economic burden are being collected for out-of-pocket expenses for both phases during in-person visits via telephone or messaging apps on a monthly basis. QoL data for patients and their primary caregivers are being collected at 3 time points (ie, time of enrollment, end of phase 1, and end of phase 2). Outcome measures are being assessed as changes in economic burden on families and changes in QoL scores. RESULTS: Participant recruitment began in July 2021. The study is ongoing and expected to be completed by March 2023. The findings based on baseline data are expected to be submitted for publication in 2023. CONCLUSIONS: This paper outlines a research proposal developed to study the impact of a PCC model for patients with DMD in low- and middle-income countries (LMICs). This study is expected to provide evidence of whether a multicomponent, patient-centric intervention could reduce economic burdens on families and improve their QoL. The results of this study could guide policy makers and health professionals in India and other LMICs to facilitate a comprehensive care program for patients living with DMD. The economic impact of a rare disease is an important consideration to formulate or evaluate any health policy or intervention related to new treatments and financial support schemes. TRIAL REGISTRATION: Clinical Trials Registry India (ICMR-NIMS) CTRI/2021/06/034274; https://www.ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=56650. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42491.

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)0.048
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.048
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.037
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0030.003
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0050.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0340.004

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 head0.179
GPT teacher head0.504
Teacher spread0.325 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations3
Published2023
Admission routes1
Has abstractyes

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