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Record W4308496499 · doi:10.1186/s12913-022-08632-9

The association between chiropractic integration in an Ontario community health centre and continued prescription opioid use for chronic non-cancer spinal pain: a sequential explanatory mixed methods study

2022· article· en· W4308496499 on OpenAlexafffundabout
Peter C. Emary, Amy L. Brown, Mark Oremus, Lawrence Mbuagbaw, Douglas F. Cameron, Jenna DiDonato, Jason W. Busse

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity of WaterlooMcMaster UniversityImpact
FundersCanadian Institutes of Health ResearchGovernment of Ontario
KeywordsMedicineChiropracticNursing researchHealth administrationCancer painMedical prescriptionPain medicinePublic healthHealth informaticsChronic painFamily medicineAlternative medicineCancerCommunity healthPhysical therapyInternal medicineNursingPsychiatryAnesthesiology

Abstract

fetched live from OpenAlex

BACKGROUND: Emerging evidence suggests that access to chiropractic care may reduce the likelihood of initiating an opioid prescription for spinal pain; however, the impact of chiropractic care for patients already prescribed opioids is uncertain. We undertook a sequential explanatory mixed methods study to evaluate the association between initiating chiropractic care and continued opioid use among adult patients attending an Ontario community health centre (CHC) and receiving opioid therapy for chronic non-cancer spinal pain. METHODS: We conducted a retrospective cohort study of 210 patient records between January 1, 2014 and December 31, 2020. We used generalized estimating equations, adjusted for patient demographics, co-morbidities, visit frequency, and calendar year, to evaluate the association between receipt versus non-receipt of chiropractic services and continued opioid use (e.g., unique opioid fills, number of refills, and dosages) up to one year following the index chiropractic visit. We also completed follow-up interviews with 14 patients and nine general practitioners from the CHC and integrated these data with our quantitative findings. RESULTS: Over 12-month follow-up, there were lower rates of opioid fills (incidence rate ratio [IRR] = 0.66; 95% confidence interval [CI], 0.52-0.83) and refills (IRR = 0.27; 95% CI, 0.17-0.42) among chiropractic recipients (n = 49) versus non-recipients (n = 161). Although patients who did and did not receive chiropractic care began the study with the same dose of opioids, recipients were less likely to be prescribed higher-dose opioids (i.e., ≥ 50 mg morphine equivalents daily) compared to non-recipients at three months (odds ratio [OR] = 0.14; 95% CI, 0.04-0.47), six months (OR = 0.14; 95% CI, 0.05-0.40), nine months (OR = 0.19; 95% CI, 0.07-0.57), and 12 months (OR = 0.22; 95% CI, 0.08-0.62). Interviews suggested that patient self-efficacy, limited effectiveness of opioids for chronic pain, stigma regarding use of opioids, and access to chiropractic treatment were important influencing factors. CONCLUSION: We found that continued prescription opioid use among patients with chronic non-cancer spinal pain who received chiropractic care was lower than in patients who did not receive chiropractic care. Four themes emerged in our qualitative interviews to help provide a richer understanding of this association. Randomized controlled trials are needed to establish the effect of chiropractic care on opioid use for chronic spinal pain.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.830

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.134
GPT teacher head0.493
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes3
Has abstractyes

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