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Record W2014686431 · doi:10.1097/nor.0000000000000050

Correlation Between Functional Status and Quality of Life After Surgery in Patients With Primary Malignant Bone Tumor of the Lower Extremities

2014· article· en· W2014686431 on OpenAlexaboutno aff
Yuan Liu, Ailing Hu, Meifen Zhang, Chenggang Shi, Xianling Zhang

Bibliographic record

VenueOrthopaedic Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Activities of daily livingPhysical therapyRehabilitationHealth related quality of lifeCorrelationInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE: This study aims to explore the correlation between functional status and quality of life after surgery in patients with primary malignant bone tumor of the lower extremities. METHODS: A total of 94 patients with primary malignant bone tumor of the lower extremities were enrolled. Correlations between their functional status and quality of life after surgery were descriptively analyzed through functional mobility assessment, Toronto extremity salvage scaling, reintegration to normal life index, and the 36-item Short Form Health Survey. RESULTS: All patients presented decreased physical function, activities of daily life (ADL), and social participation capability. Their quality of life was significantly lower than the norm. Scores under all items of functional status significantly correlated with the quality-of-life score (r = .265-.427; p < .01). The postoperative functional status of patients with primary malignant bone tumor of the lower extremities greatly influences quality of life. Lower levels of physical function, ADL, and social participation indicate poorer quality of life. CONCLUSION: To improve quality of life, necessary nursing measures should be adopted to intervene with postoperative functional rehabilitation processes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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".

Quick stats

Citations23
Published2014
Admission routes1
Has abstractyes

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