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Record W179991619 · doi:10.5055/jom.2008.0003

Do pain specialists meet the needs of the referring physician? A survey of primary care providers

2018· article· en· W179991619 on OpenAlexaff
Jane R. Wilkens, Miles J. Belgrade

Bibliographic record

VenueJournal of Opioid Management · 2018
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsStillwater (Canada)
Fundersnot available
KeywordsMedicinePrimary careFamily medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the factors that influence the use of opioids in the management of chronic noncancer pain (CNCP) by primary care providers (PCPs) for patients returning from a pain specialist. DESIGN: A survey of PCPs. SETTING: Two physician groups in the Minneapolis-St. Paul metropolitan area. PARTICIPANTS: Two seventy-six PCPs surveyed and 80 surveys returned. MAIN OUTCOME MEASURES: Participants rated the importance of specific concerns regarding the role of pain specialists and the use of opioids in the management of CNCP. Past experience with pain specialists, comfort using opioids, and opinions regarding a trilateral opioid agreement were also examined. RESULTS: The top concerns for PCPs were as follows: the use of opioids in patients with chemical dependency or psychological issues, the escalation of opioid dosing, and the use of opioids in pain states without objective findings. They also ranked highly the importance of coordinating the return of patients from a pain specialist with explicit opioid instructions and the availability of consultation by phone or a timely follow-up visit. PCPs were supportive of the concept of a trilateral opioid agreement. CONCLUSIONS: PCPs have significant concerns regarding the prescribing of opioids in CNCP. They desire closer collaboration with pain specialists, including more explicit plans of care when patients are transferred back to them. The trilateral agreement may provide one framework for better collaboration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.271
Teacher spread0.248 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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