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Record W2002017744 · doi:10.3747/co.19.882

Characterizing Distress, the 6th Vital Sign, in an Oncology Pain Clinic

2012· article· en· W2002017744 on OpenAlexaffvenue
Amy Waller, Shannon L. Groff, Neil A. Hagen, Barry D. Bultz, Linda E. Carlson

Bibliographic record

VenueCurrent Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsMedicinePsychosocialDistressContext (archaeology)AnxietyPhysical therapyAmbulatoryDepression (economics)FeelingFamily medicineInternal medicinePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

CONTEXT: The delineation of populations of cancer patients with complex symptoms can inform the planning and delivery of supportive care services. OBJECTIVES: We explored the physical, psychosocial, and practical concerns experienced by patients attending an ambulatory oncology symptom control clinic. METHODS: Patients attending a Pain Clinic at a large tertiary cancer centre were invited to complete screening measures assessing distress, pain, fatigue, anxiety, depression, and practical and psychosocial problems. A matched sample of patients who did not attend the Pain Clinic were selected as a comparison group. RESULTS: Of all eligible Pain Clinic patients, 46 (77%) completed the measures; so did 46 comparison group patients. The percentages of patients reporting distress (78.3%), pain (93.5%), and fatigue (93.5%) were higher among Pain Clinic patients than among the comparison patients. A higher percentage of Pain Clinic patients also reported multiple, severe, concurrent symptoms: 87% scored 7 or higher in at least one of the pain, fatigue, or distress scales, and 30.4% of the patients scored 7 or higher on all three. The most common problem areas were feeling a burden to others, trouble talking with friends and family, spirituality, and sleep difficulties. CONCLUSIONS: Higher levels of multiple, concurrent symptoms and psychosocial problems were found in Pain Clinic patients than in a group of patients who did not attend the Pain Clinic. Routine screening and triaging of cancer patients using a comprehensive and standardized panel of questions can facilitate symptom assessment and management, and can inform program planning.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.126
GPT teacher head0.431
Teacher spread0.305 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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