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Maddalena Opioid Switching Score in patients with cancer pain

2022· article· en· W4225298485 on OpenAlexaboutno aff
Sebastiano Mercadante, Alessio Lo Cascio, Claudio Adile, Patrizia Ferrera, Alessandra Casuccio

Bibliographic record

VenuePain · 2022
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsOpioidMedicineCancer painInternal medicineCancer

Abstract

fetched live from OpenAlex

ABSTRACT: Evaluation of opioid switching (OS) for cancer pain has not been properly assessed. The aim of this study was to assess an integrated score (Maddalena Opioid Switching Score) as a simple and repeatable tool to evaluate the outcomes of OS, facilitating the interpretation and comparison of studies, and information exchange among researchers. The integrated score took into account pain intensity, intensity of opioid-related symptoms, and cognitive function by using an author's formula. Physical and psychological symptoms were evaluated by the Edmonton Symptom Assessment Scale and Patient Global Impression (PGI) by the minimal clinically important difference. One hundred six patients were analyzed. Ninety-five patients were switched successfully, and 11 patients underwent a further OS and/or an alternative procedure. The Maddalena Opioid Switching Score significantly decreased after OS and was highly correlated to PGI of improvement ( P < 0.0005). In patients with unsuccessful OS, no significant changes in the Maddalena Opioid Switching Score and PGI were observed. A significant reduction in Edmonton Symptom Assessment Scale items intensity was observed after OS. The Maddalena Opioid Switching Score resulted to be a sensitive instrument for measuring the clinical improvement produced by OS.

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)0.003
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 score0.047
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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