Universal Precautions in Pain Medicine: A Rational Approach to the Treatment of Chronic Pain
Bibliographic record
Abstract
The heightened interest in pain management is making the need for appropriate boundary setting within the clinician-patient relationship even more apparent. Unfortunately, it is impossible to determine before hand, with any degree of certainty, who will become problematic users of prescription medications. With this in mind, a parallel is drawn between the chronic pain management paradigm and our past experience with problems identifying the "at-risk" individuals from an infectious disease model. By recognizing the need to carefully assess all patients, in a biopsychosocial model, including past and present aberrant behaviors when they exist, and by applying careful and reasonably set limits in the clinician-patient relationship, it is possible to triage chronic pain patients into three categories according to risk. This article describes a "universal precautions" approach to the assessment and ongoing management of the chronic pain patient and offers a triage scheme for estimating risk that includes recommendations for management and referral. By taking a thorough and respectful approach to patient assessment and management within chronic pain treatment, stigma can be reduced, patient care improved, and overall risk contained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".