Bibliographic record
Abstract
I am an anesthesiologist and Director of a chronic pain clinic in an academic centre in southwestern Ontario. Over the past 4 years, I have seen our waiting lists grow to 6 to 9 months and longer. In some pain clinics across Canada, the waiting list is 2 years. Understandably, this is causing widespread dissatisfaction. The longer someone suffers from pain, the less likely it is that they will have a successful return to work and function. Two years ago I was able to see six new patients a week; now it is down to three, with almost no time available for urgent requests. Although there are several reasons for burgeoning waiting lists, including the increased demands of an aging population and the retirement of pain specialists, an important part of the problem is the unwillingness of family doctors to take patients suffering from chronic pain into their practices. At our clinic, we no longer accept patients who do not have family doctors. But of the approximately 270 patients on my active roster, 40 do not have family doctors. How has this happened? It has become an almost weekly occurrence to hear of a family doctor quitting practice for such reasons as retirement, illness, moving, or changing to a less stressful type of practice. My heart sinks when patients divulge this, knowing that I have become de facto the family doctor. If they are lucky enough to locate a physician who is considering taking on new patients, they will usually fail the “screening interview.” In this process, anyone with fibromyalgia or back pain is turned down, especially if they are taking opioids. I have even had a young patient taking acetaminophen with codeine (Tylenol 3) for a first-time acute disk herniation who was refused by three family doctors. Another serious problem is that some patients who are lucky enough to have family doctors continue to need follow up at the pain clinic because their doctors refuse to prescribe opioids, even when sanctioned by a pain specialist. Recently, the Ontario Liberal government has focused on decreasing waiting lists for cancer care, joint replacements, and cardiac surgery. This type of work requires highly trained specialist teams, and it is, therefore, difficult to reduce waiting times quickly. In chronic pain management, however, with a modest amount of education, family doctors could develop the skills to continue on with prescribed medications. This would have a direct effect in reducing waiting times for pain clinics across the province. I understand that these patients are very time-consuming and have many complaints and comorbidities. As yet, there is no fee code for complex chronic pain, and this needs to be addressed. My Clinical Research Assistant, Jana Moulin, and I would welcome hearing proposed solutions to the problem of insufficient primary care for chronic pain patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.258 | 0.067 |
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".