Chronic Pain: A Case Application of a Novel Framework to Guide Interprofessional Assessment and Intervention in Primary Care
Bibliographic record
Abstract
Background: Chronic pain is a complex condition that poses challenges in assessment and treatment. Primary care teams, especially in rural areas, may have a role in managing this population, providing interprofessional care to optimize patient outcomes. Tools are needed to aid these clinicians in assessing chronic pain. Aims: The aim of this article is to present the case application of a clinical reasoning framework proposed by Walton and Elliott, which is used to identify drivers of chronic pain in a 61-year-old male patient with a remote history of spinal injury. Furthermore, it aims to demonstrate that an interprofessional, individualized intervention strategy can improve patient outcomes. Methods: This case took place in a multidisciplinary primary care team in rural northern Ontario, Canada. An assessment was completed by the author, including collection of the patient's history, a medication review, and the use of multiple validated patient-reported outcome measures (PROMs), all of which were used in applying the framework. Results: Three relevant drivers of his pain experience were identified: central nociplastic, cognitive/belief, and emotional/affective. A pharmacist and social worker then used multimodal interventions to address these drivers, which yielded improvements in scores on multiple validated pain measures but also improved the patient's self-reported quality of life. Conclusions: A clinical reasoning framework can provide a basis for identifying drivers of chronic pain during assessment and guide primary care clinicians to targeted interventions. Broader applications of this framework by primary care providers could serve to increase capacity for managing chronic pain in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".