MétaCan
Menu
Back to cohort
Record W4394856116 · doi:10.1186/s13102-024-00874-w

Effectiveness and safety of manual therapy when compared with oral pain medications in patients with neck pain: a systematic review and meta-analysis

2024· review· en· W4394856116 on OpenAlexafffund
Joshua Makin, Lauren Watson, Dimitra V Pouliopoulou, Taylor Laframboise, Bradley Gangloff, Ravinder Sidhu, Jackie Sadi, Pulak Parikh, Anita Gross, Pierre Langevin, Heather Gillis, Pavlos Bobos

Bibliographic record

VenueBMC Sports Science Medicine and Rehabilitation · 2024
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill UniversityUniversité LavalMcMaster UniversityAlberta Bone and Joint Health InstituteWestern University
FundersArthritis Society
KeywordsMedicinePhysical therapyNeck painMeta-analysisAdverse effectMEDLINERandomized controlled trialPsychological interventionCINAHLSystematic reviewRelative riskStrictly standardized mean differenceCervicogenic headacheConfidence intervalInternal medicineAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: This systematic review and meta-analysis seeks to investigate the effectiveness and safety of manual therapy (MT) interventions compared to oral pain medication in the management of neck pain. METHODS: We searched from inception to March 2023, in Cochrane Central Register of Controller Trials (CENTRAL), MEDLINE, EMBASE, Allied and Complementary Medicine (AMED) and Cumulative Index to Nursing and Allied Health Literature (CINAHL; EBSCO) for randomized controlled trials that examined the effect of manual therapy interventions for neck pain when compared to medication in adults with self-reported neck pain, irrespective of radicular findings, specific cause, and associated cervicogenic headaches. We used the Cochrane Risk of Bias 2 tool to assess the potential risk of bias in the included studies, and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to grade the quality of the evidence. RESULTS: Nine trials (779 participants) were included in the meta-analysis. We found low certainty of evidence that MT interventions may be more effective than oral pain medication in pain reduction in the short-term (Standardized Mean Difference: -0.39; 95% CI -0.66 to -0.11; 8 trials, 676 participants), and moderate certainty of evidence that MT interventions may be more effective than oral pain medication in pain reduction in the long-term (Standardized Mean Difference: - 0.36; 95% CI - 0.55 to - 0.17; 6 trials, 567 participants). We found low certainty evidence that the risk of adverse events may be lower for patients that received MT compared to the ones that received oral pain medication (Risk Ratio: 0.59; 95% CI 0.43 to 0.79; 5 trials, 426 participants). CONCLUSIONS: MT may be more effective for people with neck pain in both short and long-term with a better safety profile regarding adverse events when compared to patients receiving oral pain medications. However, we advise caution when interpreting our safety results due to the different level of reporting strategies in place for MT and medication-induced adverse events. Future MT trials should create and adhere to strict reporting strategies with regards to adverse events to help gain a better understanding on the nature of potential MT-induced adverse events and to ensure patient safety. TRIAL REGISTRATION: PROSPERO registration number: CRD42023421147.

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)0.026
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.066
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0280.051
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.344
Teacher spread0.315 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueBMC Sports Science Medicine and RehabilitationSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207