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Record W2765485166 · doi:10.5737/23688076274365374

INTERNATIONAL COLUMN: Association of demographic, economic and clinical variables in daily activities and symptoms presented by patients in cancer treatment

2017· article· en· W2765485166 on OpenAlexvenueno aff
Adriane Cristina Bernat Kolankiewicz, Tânia Solange Bosi de Souza Magnago, Angela Isabel dos Santos Düllius, Edvane Birelo Lopes De Domenico

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFunctional illiteracyConfidence intervalBivariate analysisCancerInternal medicineDemographyDiseaseCross-sectional studyPathologyStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the association between demographic, economic and clinical variables, cancer symptoms, and daily life interference in patients receiving cancer treatment in Brazil. METHODS: In this cross-sectional study, 268 patients were assessed. A questionnaire was used to collect data on demographic, economic and clinical variables, and the M.D. Anderson Symptom Inventory was used to assess cancer symptoms. Data were analyzed using bivariate and multivariate descriptive statistics. FINDINGS: The following variables were associated with higher symptom scores: female sex (prevalence ratio [PR]=1.28; 95% confidence interval [95% CI] 1.06-1.53), illiteracy or ≤ 9 years of formal education (PR=1.40; 95% CI 1.08-1.82), clinical equipment or situations that requiring nursing care (PR=1.23; 95% CI 1.03-1.46), and family history of cancer (PR=1.23; 95% CI 1.04-1.45). Daily life interference was associated with female sex (PR=1.40; 95% CI 1.12-1.75), secondary tumour (PR=1.42; 95% CI 1.16-1.74) and radiotherapy (PR=1.24; 95% CI 1.01-1.51). CONCLUSION: Management of cancer patients requires multidisciplinary knowledge, taking into consideration all the subjective dimensions of the patients. Knowing the profile of patients most strongly affected by symptoms will help them face the limitations and consequences of the disease and its treatment.

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 score0.646
Threshold uncertainty score0.999

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 head0.017
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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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