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Record W4414950545 · doi:10.1101/2025.10.05.25337352

Evaluation of a Multidimensional Assessment Tool to Simultaneously Determine Physical Activity, Nutritional and Quality of Life Status in Cancer Patients

2025· preprint· en· W4414950545 on OpenAlexaff
Sebastian Theurich, Eva Kerschbaum, Annika Tomanek, Christine Welker, Timo Niels, Nicole Erickson, Hansjörg Baurecht, Nora Zoth, Michael F. Leitzmann, Freerk T. Baumann

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity Hospital
FundersDeutsche Sporthochschule Köln
KeywordsQuality of life (healthcare)CancerHospital Anxiety and Depression ScaleBioelectrical impedance analysisAnxietyDepression (economics)Reliability (semiconductor)Physical fitness

Abstract

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Abstract Background In cancer patients, the assessment of malnutrition, muscular and neurological deficits and quality of life are usually performed independently from each other, which neglects mutual interactions, and thus, lowers the potential of supportive interventions. Therefore, we developed the “resource-oriented needs assessment (RoBa)” as a multidimensional assessment battery that simultaneously captures i) the nutritional status, ii) physical fitness and iii) the psycho-oncological status based on four validated tools or questionnaires in each domain. In a prospective, multicenter pilot study, we evaluated the feasibility and reliability of the RoBa score in real-world cancer care. Methods Consecutive cancer patients from clinical routine care at two university cancer centers were prospectively included and underwent all twelve assessments. The physical fitness domain contained the handgrip strength test, spiro-ergometry, one-leg stand test, and seven-day accelerometry. Assessments of the nutritional status domain covered the Patient-Generated Subjective Global Assessment (PG-SGA), Body-Mass-Index (BMI), Bioelectrical Impedance Analysis (BIA), and the modified Glasgow-Prognostic-Score (mGPS). The quality-of-life status was assessed by the Multidimensional Fatigue-Inventary (MFI-20), Functional Assessment of Cancer Therapy/Gynecologic Oncology Group – Neurotoxicity (FACT/GOG-Ntx), Hospital Anxiety and Depression Scale (HADS), and the EORTC QLQ-C30 questionnaire. The results of each individual assessment were scored 0 (no needs), 1 (moderate needs), or 2 (severe needs) based on published cut-off values of each assessment or international guidelines. Individual scores were summarized to domain and total RoBa scores. Correlation analyses were performed with individual, domain and total RoBa score data. Results Between 2022 and 2024 a total of 62 consecutive cancer patients (GI cancer (40.3%), non-GI cancer (59.7%) were included in to the study at two academic cancer centers in Germany. From all three RoBa domains, quality of life individual scores showed the lowest deviation from the respective domain score. Within the three domains, the two strongest correlations of individual scores with the domain score were seen for PG-SGA (r = 0.64) and BIA (r = 0.54) within the nutrition domain, spiroergometry (r = 0.85) and accelerometry (r = 0.55) within the physical fitness domain, and MFI-20 (r = 0.81) and EORTC-Q30 (r = 0.74). With regard to the total RoBa score, high correlations of each domain score were also observed: nutrition domain (r = 0.59), physical fitness (r = 0.69), and quality of life (r = 0.65). Subgroup analyses of GI versus non-GI-cancer patients revealed differences in the nutritional domain scorings Conclusion The RoBa scoring system proved feasible and strong correlations to the published assessment evaluations but also within the domain and total RoBa scoring system were observed in this pilot study. The score yielded consistent classifications regardless of the tested tumor entity, supporting its implementation for integrated estimation of support needs in nutrition, exercise, and psycho-oncology. Further research in larger cohorts is warranted.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.174
GPT teacher head0.553
Teacher spread0.379 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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