Patient Presentations in a Community Pain Clinic after COVID-19 Infection or Vaccination: A Case-Series Approach
Bibliographic record
Abstract
OBJECTIVES: Early case report studies and anecdotes from patients, medical colleagues, and social media suggest that patients may present to chronic pain clinics with a number of complaints post COVID-19 infection or vaccination. The aim of this study is to systematically report on a consecutive series of chronic pain patients seen in a community-based pain clinic, who acquired symptoms after COVID-19 infection or vaccination. METHODS: This retrospective cross-sectional descriptive study identified all patients seen at the clinic over a 4-month period (January-April 2022) with persistent symptoms after COVID-19 infection, vaccination, or both. Information was collected on sex, gender, age, details of vaccination, new pains, or exacerbation of old pain plus the development of novel symptoms. RESULTS: The study identified 21 community dwellers (17 females and 4 males; F/M 4.25/1; age range 22-79 years; mean age 46.3 years), with symptoms attributed to COVID-19 infection or vaccination. Several patients suffered exacerbation of previous pains or developed novel pains, as well as high levels of anxiety and mood disorders. A review of the existing literature provides support for the spectrum of symptoms displayed by the study group. CONCLUSIONS: Information collected in this study will add to the body of COVID-19-related literature and assist particularly community practitioners in recognizing and managing these conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".