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Record W2906312479 · doi:10.4103/ijpc.ijpc_12_18

Quality of Life and Neuropathic Pain in Hospitalized Cancer Patients: A Comparative Analysis of Patients in Palliative Care Wards Versus Those in General Wards.

2018· article· en· W2906312479 on OpenAlexaboutno aff
Ulaş Sungur, Sibel Eyigör, İsmail Caramat

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePalliative careNeuropathic painQuality of life (healthcare)Context (archaeology)Brief Pain InventoryCancer painHospital Anxiety and Depression ScaleAnxietyCancerDepression (economics)Physical therapyInternal medicineChronic painPsychiatryAnesthesiaNursing

Abstract

fetched live from OpenAlex

CONTEXT: While the survival of cancer patients is prolonged due to the development of new treatment strategies and advancing technologies, the prevalence of symptoms such as neuropathic pain affecting the quality of life is also increasing. AIMS: The aim of this study is to determine the relationship between neuropathic pain (NP) and quality of life in hospitalized cancer patients and to compare patients in general wards and those in palliative care wards in terms of NP and quality of life. SUBJECTS AND METHODS: A total of 156 patients, 53 cancer patients hospitalized in the palliative care unit and 103 cancer patients hospitalized in general wards, were included in the study. The Douleur Neuropathic 4 test was used for NP assessment, and the Edmonton Symptom Assessment System (ESAS), Hospital Anxiety and Depression Scale (HAD), Brief Fatigue Inventory (BFI), and Short Form of Brief Pain Inventory (SF-BPI) were used for assessing pain characteristics and their effects on quality of life. RESULTS: < 0.05). CONCLUSIONS: Since there was a homogeneous distribution among the groups in terms of both cancer treatment and pain management, we directly related the deterioration of the patients' quality of life to NP.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.057
GPT teacher head0.336
Teacher spread0.279 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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