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Record W4404194118 · doi:10.1136/spcare-2024-005147

Comparison of a novel methadone rotation method with other commonly used methods

2024· article· en· W4404194118 on OpenAlexaboutno aff
Elaine Cunningham, Nicole DiBiagio, F. Connell, Maedhbh Flannery, Michael Cronin, Marie Murphy, Mary Jane O’Leary, Fiona Kiely, Aoife C Lowney

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMethadoneRotation (mathematics)Computer sciencePsychologyArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare a novel method of methadone rotation used in a specialist palliative care inpatient unit (SPCU) in Cork, Ireland, with rapid titration methods using Perth and Brisbane Protocols as well as the Edmonton method of methadone rotation. METHODS: A retrospective chart review was performed in March-June 2022. All patients who completed rotation to methadone during 2018-2019 in the SPCU were included. 2018-2019 was selected to study a population not affected by the coronavirus pandemic. Oral morphine equivalent (OME) was calculated using the opioid conversion chart. From the OME, the expected daily methadone dose was calculated using the Perth, Brisbane and Edmonton methods. These figures were then compared directly with the actual methadone doses achieved using our dosing schedule. RESULTS: A comparison of the expected doses using the Perth and Brisbane rapid titration protocols and stable daily dose achieved revealed that the stable methadone dose was significantly lower than both rapid titration protocols (p=<0.0001) and (p=0.0035, respectively). However, a comparison of the expected dose using the Edmonton method and the dose achieved did not determine any significant difference (p=0.7602). CONCLUSIONS: This is the first evaluation of a novel Irish method of methadone rotation and demonstrates a lower overall daily methadone dose compared with established protocols.

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: Methods · Consensus signal: none
Teacher disagreement score0.513
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.125
GPT teacher head0.505
Teacher spread0.380 · 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
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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