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Record W4328053844 · doi:10.46747/cfp.6903e52

Chronic noncancer pain management

2023· article· en· W4328053844 on OpenAlexaffvenueabout
Metasebia Assefa, Isabelle LeClerc, Elizabeth Muggah, Raywat Deonandan, Charles Godbout, Hillel M. Finestone

Bibliographic record

VenueCanadian Family Physician · 2023
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsÉlisabeth Bruyère HospitalUniversity of OttawaBruyère
Fundersnot available
KeywordsMedicineChronic painPsychosocialMultidisciplinary approachPhysical therapyPain managementMEDLINEPrimary careFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

PROBLEM ADDRESSED: Chronic noncancer pain is often excessively managed with medications (most notably opioids) instead of nonpharmacologic options or multidisciplinary care-the gold standards. OBJECTIVE OF PROGRAM: To offer an effective alternative to pharmacologic management of chronic noncancer pain in primary care. PROGRAM DESCRIPTION: Patients 18 years of age or older with chronic noncancer pain were referred by family physicians or nurse practitioners in a family health team (outpatient, multidisciplinary clinic) in Ottawa, Ont. A registered nurse used the Pain Explanation and Treatment Diagram with patients, taught self-management skills (related to habits [smoking, consumption of alcohol, diet], exercise, sleep, ergonomics, and psychosocial factors), and referred patients to relevant resources. CONCLUSION: A nurse-led chronic pain program, initiated without extra funding, was successfully integrated into a primary care setting. Among the participating patients in the pilot project, outcomes related to pain intensity, pain interference with daily living, and opioid use were encouraging. This program could serve as a model for improving chronic noncancer pain management in primary care.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.017
GPT teacher head0.244
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations13
Published2023
Admission routes3
Has abstractyes

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