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Record W4406200609 · doi:10.1371/journal.pone.0313207

Impact of functional re-education and environmental adaptation in cancer patients with respiratory pathology: Study protocol

2025· article· en· W4406200609 on OpenAlexfundno aff
Eduardo José Fernández Rodríguez, María Isabel Rihuete Galve, Alberto García Martín, Celia Sánchez Gómez, Fausto J. Barbero Iglesias, Roberto Méndez‐Sánchez, Ana Silvia Puente-González, Susana Sáez-Gutiérrez, Emilio Fonseca‐Sánchez, Juan Jesús Cruz

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
FundersBristol-Myers Squibb CanadaBristol-Myers Squibb
KeywordsMedicinePsychological interventionQuality of life (healthcare)Randomized controlled trialProtocol (science)Intervention (counseling)Health careClinical trialIntensive care medicinePhysical therapyInternal medicineAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: In recent years, cancer survival rates have increased exponentially. However, this rise in survival comes with a significant drawback. As the number of treatment lines has grown, so too have the side effects, which can severely impact patients' functionality and quality of life. One of the most concerning effects is dyspnoea, a serious health issue that imposes substantial limitations on individuals. While traditional clinical approaches, primarily focused on pharmacological interventions, are commonly employed to manage dyspnoea, we argue that these methods may not always provide optimal symptomatic relief. Therefore, we propose a study protocol to implement an interdisciplinary intervention for these patients. This protocol aims to enhance standard clinical practice by introducing a program of functional re-education and environmental adaptation. We believe this intervention is essential for the follow-up care of patients with respiratory conditions after hospital discharge. METHODS: A two-arm, randomized, parallel, controlled clinical trial will be conducted by the University of Salamanca, Spain, in collaboration with the Medical Oncology Service of the University Health Care Complex of Salamanca. The trial aims to recruit 80 oncology patients who exhibit symptoms of dyspnoea during hospital admission. Participants will be randomly assigned to one of two groups: the control group, which will receive a health education program, and the experimental group, which will receive both the health education program and a functional re-education and environmental adaptation program. Assessments will be conducted at three time points: baseline (prior to hospital discharge), follow-up (15 days after discharge), and the end of the intervention (1 month after discharge). During these assessments, sociodemographic data will be collected, and the following scales will be administered: Barthel Index, Medical Research Council Dyspnoea Scale (mMRC), EuroQol 5-D questionnaire (EQ-5D), Visual Analogue Scale (VAS), Short Physical Performance Battery (SPPB), Tampa Scale for Kinesiophobia (TSK), and the Zarit Reduced Caregiver Burden Scale. DISCUSSION: The findings of this research can be easily implemented in clinical settings by introducing a targeted intervention to improve the quality of life for this patient population. This study aims to advance traditional clinical approaches by facilitating the recovery or adaptation of cancer patients in their daily routines, in relation to the severity of their symptoms. Our primary goal is to enhance independence and functionality, ultimately improving their overall standard of living. TRIAL REGISTRATION: ClinicalTrials.gov; ID: NCT06035263. Registered on: September 19, 2023. https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S000DM2U&selectaction=Edit&uid=U0004OJ7&ts=6&cx=f7qqxo.

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)0.020
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.015
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0410.007

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 head0.041
GPT teacher head0.335
Teacher spread0.293 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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