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Record W2332906996 · doi:10.1097/hnp.0000000000000087

Pain and Fatigue in Elderly Cancer Patients

2015· article· en· W2332906996 on OpenAlexaboutno aff
Melek Erturk, Yasemin Yıldırım, Serap Parlar Kılıç, Serap Özer Yaman, Fisun Şenuzun Aykar

Bibliographic record

VenueHolistic Nursing Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyIntensity (physics)McGill Pain QuestionnaireCancerInternal medicineVisual analogue scale

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the pain and fatigue levels of elderly cancer patients and to examine whether pain is an independent variable in fatigue development. A total of 250 elderly cancer patients undergoing treatment in the outpatient chemotherapy units and internal medicine clinic at 2 hospitals were enrolled. A "Patient Information Form," the "McGill Melzack Pain Questionnaire," and the "Brief Fatigue Inventory" were used as data instruments. It was determined that all patients had pain and that the mean present pain intensity score was 2.70 ± 0.99, the mean worst pain intensity score was 4.40 ± 0.86, and the mean least pain intensity score was 1.40 ± 0.66. Whereas the existing fatigue severity score of the patients with fatigue (43.6%) was 6.27 ± 2.06, the mean usual fatigue severity of the patients in the last 24 hours was 6.19 ± 1.63 and that the mean worst fatigue severity score in the last 24 hours was 7.29 ± 1.57. When the regression analysis carried out between the pain and fatigue intensities is examined, it was determined that pain is an independent variable in increasing fatigue and that there is a statistically significant relationship (P < .05). It is important that nurses develop strategies to prevent and determine activities to decrease the pain and fatigue of the patients while planning and implementing their holistic care in a relevant manner.

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)0.005
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 score0.355
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.085
GPT teacher head0.389
Teacher spread0.304 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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