A Guide for Cancer Pain Management in Latin America
Bibliographic record
Abstract
Cancer prevalence in Latin America (LATAM) is increasing and represents a major cause of morbidity and mortality. Managing cancer patients—who live longer than ever before—requires appropriate management of cancer pain, described by the World Health Organization (WHO) in 1988 with its now famous “pain ladder,” the rungs of which represented nonopioids, weak opioids, and strong opioids as pain relievers. Yet even today much cancer pain is undertreated. Cancer pain can be multimechanistic with a neuropathic component which may complicate pain control. Acute pain should be treated aggressively to avoid the potential transition to chronic pain, a maladaptive form of pain that can be particularly challenging to treat. Although opioids have been recognized by WHO in 1988 and since then as a safe, effective form for treating moderate to severe cancer pain, opioid consumption in LATAM nations is very low. LATAM countries make up about 9% of the world’s population but represent only about 1% of global opioid consumption. Better education about pain control in cancer and opioid therapy is needed by both healthcare providers and patients to better treat cancer pain in LATAM. But opioid-associated side effects and the risk of abuse and diversion are important risks of opioid therapy that are to be fully understood by both healthcare providers and patients before commencing therapy. Opioid risk management plans balance the need for access to opioids for appropriate patients with the mitigation of opioid-related risks of abuse and addiction. Risks as well as benefits should be clearly understood in order to consider opioid therapy. Combining education, prescription drug monitoring plans, and other risk mitigation strategies may be useful tools. Abuse-deterrent formulations, such as fixed-dose combination products of an opioid with naloxone, have been designed to resist abuse. LATAM may benefit from such new products in efforts to bring better pain control to cancer patients in a rational and responsible manner.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.052 |
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".