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Record W3119086053 · doi:10.1186/s12998-020-00357-y

Effects of spinal manipulative therapy on inflammatory mediators in patients with non-specific low back pain: a non-randomized controlled clinical trial

2021· article· en· W3119086053 on OpenAlexafffund
Julita A. Teodorczyk-Injeyan, John J. Triano, Robert Gringmuth, Christopher DeGraauw, Adrian Chow, H. Stephen Injeyan

Bibliographic record

VenueChiropractic & Manual Therapies · 2021
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCanadian Memorial Chiropractic College
FundersCanadian Memorial Chiropractic College
KeywordsMedicineAsymptomaticInternal medicineLow back painManual therapyRandomized controlled trialStatistical significanceTumor necrosis factor alphaPhysical therapyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The inflammatory profiles of patients with acute and chronic nonspecific low back pain (LBP) patients are distinct. Spinal manipulative therapy (SMT) has been shown to modulate the production of nociceptive chemokines differently in these patient cohorts. The present study further investigates the effect(s) of SMT on other inflammatory mediators in the same LBP patient cohorts. METHODS: Acute (n = 22) and chronic (n = 25) LBP patients with minimum pain scores of 3 on a 10-point numeric scale, and asymptomatic controls (n = 24) were recruited according to stringent exclusion criteria. Blood samples were obtained at baseline and after 2 weeks during which patients received 6 SMTs in the lumbar or lumbosacral region. The in vitro production of tumor necrosis factor (TNFα), interleukin-1 β (IL-1β), IL-6, IL-2, interferon ɣ (IFNɣ), IL-1 receptor antagonist (IL-1RA), TNF soluble receptor type 2 (sTNFR2) and IL-10 was determined by specific immunoassays. Parametric as well as non-parametric statistics (PAST 3.18 beta software) was used to determine significance of differences between and within study groups prior and post-SMT. Effect size (ES) estimates were obtained using Cohen's d. RESULTS: Compared with asymptomatic controls, SMT-related change scores were significant (P = 0.03-0.01) in reducing the production levels of TNFα in both patient cohorts and those of IL-6, IFNɣ and sTNFR2 (P = 0.001-0.02) in patients with chronic LBP. Above-moderate to large ES (d > 0.6-1.4) was observed for these mediators. Compared with respective baselines, a significant post-SMT reduction (P = 0.01) of IL-6 production was detected only in patients with chronic LBP while a significant increase of IL-2 production (P = 0.001 vs. control, and P = 0.004 vs. chronic LBP group) and a large ES (d = 0.87) were observed in patients with acute LBP. Pain and disability scores declined significantly (P < 0.001) in all LBP patients, and were positively correlated (P = 0.03) with IFNɣ and IL-2 levels in the acute LBP cohort. CONCLUSION: The short course of SMT treatments of non-specific LBP patients resulted in significant albeit limited and diverse alterations in the production of several of the mediators investigated in this study. This exploratory study highlights the potential of SMT to modulate the production of inflammatory components in acute and chronic non-specific LBP patients and suggests a need for further, randomized controlled clinical trials in this area. TRIAL REGISTRATION: This study was prospectively registered April 2012 with Clinical Trials.gov ( #NCT01766141 ). https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S0003ZIL&selectaction=Edit&uid=U0001V74&ts=2&cx=-axvqtg.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.020
GPT teacher head0.318
Teacher spread0.299 · 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 designNon-randomized trial
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

Citations33
Published2021
Admission routes2
Has abstractyes

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