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Record W4312254045 · doi:10.25215/0602.047

A Study on Analgesic Effect of Music Interventions after Chemotherapy or Radiotherapy in Cancer Patients

2018· article· en· W4312254045 on OpenAlexaboutno aff
Farnaz Dehkhoda, Seema Vinayak

Bibliographic record

VenueInternational Journal of Indian Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMusic therapyRadiation therapyChemotherapyAnalgesicCancer painCancerPhysical therapyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

One of the most considerable side effects of chemotherapy and radiotherapy in cancer patients is pain. The pain that caused by these treatments can include muscle pain, stomach pain, headaches and pain caused by nerve damage. These pains can get better after treatment sessions but in some patients, permanent nerve damage cause severe symptoms after treatment. The present study examines the palliative efficacy of active and receptive music therapy cancer patients after chemotherapy or radiotherapy. 184 young adult cancer patients in age range of 20-40 years, who were undergoing chemotherapy or radiotherapy, have been studied inactive and receptive music therapy intervention groups, and a control group. Participants were questioned by McGill pain questionnaire visual analogue scale in pre-test and post-test after 10 sessions of active or receptive music therapy(with each session of 15-30 minutes). Results indicated significant differences in reduction in scores of pain from pre-therapy to post-therapy scores for both intervention groups as compared to no intervention group. Analyses of Covariance applied to compare these three independent groups revealed that active music therapy had the greatest impact on the reduction of pain as compared to the receptive music therapy group. The study has great implications for analgesic effect of music therapy in cancer patients during chemotherapy or radiotherapy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.054
GPT teacher head0.476
Teacher spread0.422 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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