Use of Opioids in Latin America: The Need of an Evidence-Based Change
Bibliographic record
Abstract
OBJECTIVE: The subject of this publication has been focused on local considerations for facilitating regional best practice, including identifying and uniformly adopting the most relevant international guidelines on opioid use (OU) in chronic pain management. DESIGN AND SETTING: The Change Pain Latin America (CPLA) Advisory Panel conducted a comprehensive, robust, and critical analysis of published national and international reviews and guidelines of OU, considering those most appropriate for Latin America. METHODS: A PubMed search was conducted using the terms "opioid," "chronic," and "pain" and then refined using the filters "practice guidelines" and "within the last 5 years" (2007-2012). Once the publications were identified, they were selected using five key criteria: "Evidence based," "Comprehensive," "From a well-recognized source," "Current publications," and "Based on best practice" and then critically analyzed considering 10 key criteria for determining the most relevant guidelines to be applied in Latin America. RESULTS: The initial PubMed search identified 177 reviews and guidelines, which was reduced to 16 articles using the five preliminary criteria. After a secondary analysis according to the 10 key criteria specific to OU in Latin America, 10 publications were selected for critical review and discussion. CONCLUSIONS: The CPLA advisory panel considered the "Safe and effective use of opioids for chronic non-cancer pain" (published in 2010 by the NOUGG of Canada) to be valid, relevant to Latin America, practical, evidence-based, concise, unambiguous, and sufficiently educational to provide clear instruction on OU and pain management and, thus, recommended for uniform adoption across the Latin America region.
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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 | 0.123 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".