Emergence From the COVID-19 Pandemic and the Care of Chronic Pain: Guidance for the Interventionalist
Bibliographic record
Abstract
BACKGROUND: The current coronavirus disease 2019 (COVID-19) pandemic led to a significant disruption in the care of pain from chronic and subacute conditions. The impact of this cessation of pain treatment may have unintended consequences of increased pain, reduced function, increased reliance on opioid medications, and potential increased morbidity, due to the systemic impact of untreated disease burden. This may include decreased mobility, reduction in overall health status, and increase of opioid use with the associated risks. METHODS: The article is the study of the American Society of Pain and Neuroscience (ASPN) COVID-19 task force to evaluate the policies set forth by federal, state, and local agencies to reduce or eliminate elective procedures for those patients with pain from spine, nerve, and joint disease. The impact of these decisions, which were needed to reduce the spread of the pandemic, led to a delay in care for many patients. We hence review an emergence plan to reinitiate this pain-related care. The goal is to outline a path to work with federal, state, and local authorities to combat the spread of the pandemic and minimize the deleterious impact of pain and suffering on our chronic pain patients. RESULTS: The article sets forth a strategy for the interventional pain centers to reemerge from the current pandemic and to set a course for future events. CONCLUSIONS: The COVID-19 pandemic represents an overwhelming challenge to interventional pain physicians and their patients. In addition to urgent actions needed for disease mitigation, the ASPN recommends a staged return to pain management professionals' workflow.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".