Patient self-reported musculoskeletal symptoms before and after the interruption of chiropractic care during the COVID-19 lockdown in Ontario, Canada: a retrospective case series.
Bibliographic record
Abstract
Purpose: To describe characteristics and course of chiropractic patients' self-reported musculoskeletal (MSK) symptoms following interruption of chiropractic treatment during the COVID-19 lockdown. Methods: Using a retrospective case series design, patient demographic, clinical and patient-reported clinical outcomes variables were abstracted from electronic health records of patients attending a chiropractic teaching clinic. We measured self-perceived changes in symptoms cross-sectionally at each of two time points: before and after the COVID-19 lockdown. Results: 133 of 184 patients were eligible. Most had comorbidities and treatment for multiple MSK diagnoses pre-lockdown. Based on patients' self-perception, 17% improved (vs 77% pre-lockdown), 23% did not change (vs 17% pre-lockdown) and 43% worsened (vs 5% pre-lockdown) in MSK symptoms during lockdown. Those reporting worsening post-lockdown had more treatments, longer period of treatment time pre-lockdown, and more severe pain (mean: 7/10) post-lockdown. Upon clinic reopening, 47% of patients returned for care, more often reporting worsened MSK symptoms and higher average pain score (6.2/10) than non-returning patients (3.9/10). Summary: Some patients experiencing interruptions in chiropractic care during COVID-19 lockdown returned with worsened MSK symptoms, while others showed improvement and did not return to clinic. Our study helps generate future research hypotheses regarding the contribution of chiropractic treatment (e.g., during pandemics).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".