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Record W2783168131 · doi:10.4103/ijpc.ijpc_182_17

When to use methadone for pain: A case-based approach

2018· review· en· W2783168131 on OpenAlexaff
Gayatri Palat, Nandini Vallath, Srini Chary, Ann Broderick

Bibliographic record

VenueIndian Journal of Palliative Care · 2018
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineMethadoneCancer painOpioidContext (archaeology)Multidisciplinary approachNeuropathic painNormativeMultidisciplinary teamPsychiatryAnesthesiaCancerNursingInternal medicine

Abstract

fetched live from OpenAlex

The case studies are written in this article to illustrate how methadone might be used for pain in the Indian context. These cases might be used for discussion in a multidisciplinary team, or for individual study. It is important to understand that pain requires a multidisciplinary approach as opioids will assist only with physical, i.e. neuropathic and nociceptive pain, but not emotional, spiritual, or relational pain or the pain of immobility. The social determinants of pain were included to demonstrate how emotional, relational, and psychological dimensions of pain amplify the physical aspects of pain. The case studies follow a practical step-wise approach to pain while undergoing cancer treatment, pain toward the end-of-life and needing longer acting opioid. Methadone in children, and methadone in conditions of opioid toxicity or where there is a need for absorption in the proximal intestine cases are included.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.141
GPT teacher head0.383
Teacher spread0.241 · 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
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

Citations1
Published2018
Admission routes1
Has abstractyes

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