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Record W2168422120 · doi:10.2466/pr0.2001.89.1.3

Pain Language of Bone Marrow Transplantation Patients

2001· article· en· W2168422120 on OpenAlexaboutno aff
Samuel M. Y. Ho, David Horné, Jeff Szer

Bibliographic record

VenuePsychological Reports · 2001
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
Fundersnot available
KeywordsHypnosisMedicineTransplantationMcGill Pain QuestionnaireCognitionCoping (psychology)Intervention (counseling)Physical therapyBone marrow transplantationPain catastrophizingPsychologyChronic painClinical psychologySurgeryPsychiatryVisual analogue scaleAlternative medicinePathology

Abstract

fetched live from OpenAlex

Previous studies have shown that hypnosis may be effective in reducing intensity of pain among bone marrow transplantation patients whereas cognitive behavioral intervention without imagery was not effective for this group of patients. Since hypnosis alters patients' perception of pain and cognitive behavioral intervention changes patients' beliefs and improves their coping with pain, we hypothesized that sensory pain is more important than affective pain in understanding the pain experience of patients undergoing bone marrow transplantation. To test this hypothesis we administered the McGill Pain Questionnaire longitudinally to 50 consecutive eligible recipients of bone marrow transplantation during hospitalization to assess the different dimensions of pain they experienced. Consistent with our hypothesis, sensory pain fluctuated with treatment stages, and the pattern was consistent with previous findings. Patients reported significantly higher sensory pain than affective pain at all assessment points. In contrast, affective pain remained low and stable throughout the treatment. Our results contribute to the understanding of the nature of pain in bone marrow transplantation and suggest pain management strategies that focus on sensory pain as in hypnosis are more useful for such patients.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.489
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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