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Record W2979102446 · doi:10.1007/s12630-019-01482-w

Interventional anesthesia and palliative care collaboration to manage cancer pain: a narrative review

2019· review· en· W2979102446 on OpenAlexaff
Jenny Lau, David Flamer, Patricia Murphy-Kane

Bibliographic record

VenueCanadian Journal of Anesthesia/Journal canadien d anesthésie · 2019
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsMount Sinai HospitalToronto Western HospitalPrincess Margaret Cancer CentreHome and Community Care Support ServicesUniversity Health Network
Fundersnot available
KeywordsMedicinePalliative careCancer painIntensive care medicineAnalgesicCancerPain managementNarrative reviewPain medicineInterventional pain managementAnesthesiaNursingAnesthesiologyInternal medicine

Abstract

fetched live from OpenAlex

Pain is a common symptom associated with advanced cancer. An estimated 66.4% of people with advanced cancer experience pain from their disease or treatment. Pain management is an essential component of palliative care. Opioids and adjuvant therapies are the mainstay of cancer pain management. Nevertheless, a proportion of patients may experience complex pain that is not responsive to conventional analgesia. Interventional analgesia procedures may be appropriate and necessary to manage complex, cancer-related pain. This narrative review uses a theoretical case to highlight core principles of palliative care and interventional anesthesia, and the importance of collaborative, interdisciplinary care. An overview and discussion of pragmatic considerations of peripheral nervous system interventional analgesic procedures and neuraxial analgesia infusions are provided.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.321
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2019
Admission routes1
Has abstractyes

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