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Record W4391350727 · doi:10.3390/cancers16030568

Pain Catastrophizing in Cancer Patients

2024· article· en· W4391350727 on OpenAlexaboutno aff
Sebastiano Mercadante, Patrizia Ferrera, Alessio Lo Cascio, Alessandra Casuccio

Bibliographic record

VenueCancers · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerInternal medicine

Abstract

fetched live from OpenAlex

Background: Pain catastrophizing is a group of negative irrational cognitions in the context of anticipated or actual pain. The aim of this study was to decipher the possible role of catastrophism on pain expression and outcomes after a comprehensive palliative care treatment. Methods: A consecutive sample of patients with uncontrolled pain was assessed. Demographic characteristics, symptom intensity included in the Edmonton symptom assessment system (ESAS), and opioid drugs used were recorded at admission (T0). The Pain Catastrophizing Scale (PCS) was measured for patients. Patients were also asked about their personalized symptom goal (PSG) for each symptom of ESAS. One week after a comprehensive palliative care treatment (T7), ESAS and opioid doses used were recorded again, and the number of patients who achieved their PSG (PSGR) were calculated. At the same interval (T7), Minimal Clinically Important Difference (MCID) was calculated using patient global impression (PGI). Results: Ninety-five patients were eligible. A significant decrease in symptom intensity was reported for all ESAS items. PGI was positive for all symptoms, with higher values for pain, poor well-being, and poor sleep. Only the rumination subscale of catastrophism was significantly associated with pain at T0 (B = 0.540; p = 0.034). Conclusions: Catastrophism was not associated with the levels of pain intensity, PSG, PSGR, and PGI for pain, except the rumination subscale that was associated with pain intensity at T0. A comprehensive palliative care management provided the relevant changes in symptom burden, undoing the pain expression associated with rumination.

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 score0.374
Threshold uncertainty score0.286

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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