Translation, Cultural Adaptation and Validation of the Current Opioid Misuse Measure for European Portuguese
Bibliographic record
Abstract
INTRODUCTION: Current practice guidelines recommend using Current Opioid Misuse Measure to screen aberrant opioid-related behaviors in chronic pain patients. Our aims were to translate, adapt and validate it to be used in Portuguese chronic pain patients. MATERIAL AND METHODS: Translation and cultural adaptation process followed guidelines and a model of principles for good practice. Adult chronic pain patients on opioid therapy, from one major hospital in Portugal, were invited to complete the translated version. Descriptive statistics, Cronbach's alpha, inter-item, item-total and intra-class correlation coefficients and principal components analysis were applied. RESULTS: Translation process was performed as planned and the validation sample was composed by 98 patients (median age = 62.5 years). Regarding internal consistency, a global Cronbach's alpha of 0.778 was obtained and item-total correlations of all items were above 0.20 with four exceptions. An intra-class correlation coefficient of 0.90 was found between test and retest. Regarding validity, all 17 items presented a content validity index above 0.80. Six principal components were extracted and explained 66.3% of the variance. DISCUSSION: The Portuguese version of Current Opioid Misuse Measure was properly translated, adapted and validated; showing good quality in terms of reliability and validity. This is the first instrument to screen aberrant opioid-related behaviors in Portuguese chronic pain patients. Consequently, it will aid and promote the identification of opioid misuse in these patients. CONCLUSION: The implementation of this questionnaire may reduce the incidence and morbimortality of opioid misuse among chronic pain patients and should improve chronic pain treatment in Portugal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".