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Record W2955669124 · doi:10.3747/co.26.4517

Framing of the Opioid Problem in Cancer Pain Management in Canada

2019· article· en· W2955669124 on OpenAlexafffundvenueabout
Rashi Asthana, Shannon Goodall, Jenny Lau, Camilla Zimmermann, Patrick Diaz, A. B. Wan, Edward Chow, Carlo DeAngelis

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersJoey and Mary Furfari Cancer Research Fund
KeywordsMedicineChronic painGuidelineCancer painOpioidHydromorphoneIntensive care medicineCancerPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

and the European Pain Federation position paper on appropriate opioid use in chronic pain management. Though the target populations for the guidelines are the same, their recommendations differ depending on their purpose. The intent of the Canadian guideline is to reduce the incidence of serious adverse effects. Its goal was therefore to set limits on the use of opioids. In contrast, the European Pain Federation position paper is meant to promote safe and appropriate opioid use for chronic pain. The content of the two guidelines could have unintentional consequences on other populations that receive opioid therapy for symptom management, such as patients with cancer. In this article, we present expert opinion about those chronic pain management guidelines and their impact on patients with cancer diagnoses, especially those with histories of substance use disorder and psychiatric conditions. Though some principles of chronic pain management can be extrapolated, we recommend that guidelines for cancer pain management should be developed using empirical data primarily from patients with cancer who are receiving opioid therapy.

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 score0.583
Threshold uncertainty score0.811

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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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

Citations9
Published2019
Admission routes4
Has abstractyes

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