Bibliographic record
Abstract
Chronic non-cancer pain (CNCP) in adults is one of the most common reasons for which patients seek medical help. Chronic pain is present in about 20% of the world's adult population and as a global health problem requires greater attention from every society. Chronic pain has a negative impact not only on the individual but by increasing costs, directly on the health system and indirectly on the economy of the whole society. Its adequate treatment is a human right, and every healthcare system must ensure it. In this regard, great progress has been made with the implementation of chronic pain in the revised ICD-11, which will contribute to changing health policy and focusing more attention on the prevention and treatment of chronic pain worldwide. Integrative pharmacological and nonpharmacological therapeutic approaches with the patient in focus (patient-centric approach) have the strongest evidence of effectiveness; because they reduce not only the intensity of pain but also improve physical, psychological, and social functionality and increase patient satisfaction. Individually tailored balanced pharmacological approaches for different phenotypes of chronic pain (nociceptive, neuropathic, nociplastic) involve the use of nonselective and selective non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, antidepressants, anticonvulsants, other adjuvant therapies and opioid analgesics. These pharmacological approaches based on mechanisms, intensity of pain, and comorbidities, contribute to the optimization of individual therapeutic goals and the maximization of safety and quality of life of persons being treated. Liberalization of opioid prescription in CNCP and inadequate selection and follow-up of patients have contributed to opioid prescription reaching epidemic proportions in the USA, Canada, and some Western European countries and led to the phenomenon of medicalization, iatrogenesis, and fatal outcomes, i.e. opioid crisis. The U.S. Centers for Disease Control (CDC, 2022) guideline for opioid prescribing is summarized in 12 key recommendations based on strong evidence and related to initiation of opioid therapy, opioid selection, dose determination, duration of therapy, monitoring, and assessment of potential side effects from the use of opioids.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".