Supporting Better Access to Chronic Pain Specialists: The Champlain BASE <sup>™</sup> eConsult Service
Bibliographic record
Abstract
INTRODUCTION: (Building Access to Specialists through eConsultation) eConsult service can improve access to specialist care for patients with chronic pain by facilitating electronic communication between primary care providers (PCPs) and specialists. We explored the content of eConsult cases sent to chronic pain specialists to identify the major themes emerging from exchanges between PCPs and specialists regarding patients with chronic pain. METHODS: We conducted a thematic analysis of eConsult cases submitted to chronic pain specialists between April 1, 2011 and October 31, 2014, using a constant comparison approach. RESULTS: PCPs submitted 128 cases to chronic pain specialists during the study period. The study team coded 48 cases before data saturation was reached. PCPs sought advice for treating patients with chronic pain arising from a range of medical problems, and who frequently struggled with issues of mental health, substance dependence, and social complexity. Specialists responded with advice on pain management and treatment, directed PCPs to published guidelines and community resources, and validated the PCPs' frustration or concerns. Specialists provided instruction on safe opioid prescribing and how to identify and manage potential cases of substance dependence. CONCLUSION: Providing care to patients with chronic pain is a challenge for PCPs, who often experience frustration at their inability to provide a definitive solution for patients. Specialists offered invaluable feedback not only through guidance and advice, but also with sympathy and encouragement.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".