Bibliographic record
Abstract
BACKGROUND: Pain remains one of the top five reasons for consultations in general practice, presenting either alone or as comorbidity. The World Health Organization (WHO) analgesic ladder proposed in 1986 has been the cornerstone of pain management, but is often inadequate in daily practice, especially when dealing with the diverse nature and etiology of various pain conditions. There is a need for a better concept which is universally applicable that acknowledges the value of, and need for, other domains of treatment for pain. OBJECTIVE: This article reviews the original ideas of the WHO analgesic ladder and proposes its extension to a platform model in the context of pain management. DISCUSSION: Pain affects both the physical and psychological wellbeing of patients and should not be treated with pharmacotherapy alone. The model of WHO analgesic ladder provides guidelines for choosing the analgesic agents, but has its limitations. Incorporating the latest paradigm of neuromatrix theory, both acute and chronic pain should be best managed with a broader perspective incorporating multimodal non-pharmacological and supportive treatments, illustrated by the concept of interacting domains on a broad platform as presented in this article. Different levels of pain severity and chronicity necessitate different analgesic platforms of management, and the clinician should move up or down the appropriate platform to explore the various treatment options as per the status and needs of the patient. KEYWORDS: Analgesic ladder; pain management; analgesic platform
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".