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Record W4407775962 · doi:10.1186/s12891-025-08407-3

Efficacy of non-surgical, non-pharmacological treatments for congenital muscular torticollis: a systematic review and meta-analysis

2025· review· en· W4407775962 on OpenAlexafffund
Joyaa B. Antares, Mark Jones, Nga Ting Natalie Chak, Yuan Chi, Hong Li, Mingdi Li, Tracy Mui Kwan Chen, Crystal Lee, Donna M. Urquhart

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsMcMaster UniversityImpact
FundersQueen's UniversityUniversity of BristolQueen's University Belfast
KeywordsMedicineSports medicineMeta-analysisOrthopedic surgeryTorticollisRheumatologyPhysical therapyPhysical medicine and rehabilitationRehabilitationMEDLINESurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Congenital Muscular Torticollis (CMT) is the third most common musculoskeletal condition in infancy, and if untreated can lead to significant disability. While a range of conservative treatments are commonly used in the management of CMT, an understanding of their efficacy and safety is limited. This systematic review and meta-analysis, without language or discipline restriction, was conducted to address this knowledge gap. METHODS: Electronic searches of CENTRAL, PubMed, 22 other electronic databases, three trials registers and Google Scholar, were conducted for randomised controlled trials, which examined any non-surgical, non-pharmacological interventions, including but not limited to manual treatments, movement therapy, acupuncture, adjunctive therapies and physical support, in children aged 0 to 5 years with CMT. Two reviewers independently assessed the risk of bias of the included studies using the Cochrane Risk of bias 1 tool, rated their certainty of evidence using grading of recommendations assessment, development and evaluation (GRADE) framework, and performed random-effects meta-analyses. RESULTS: One hundred studies (80 from China) involving 8125 participants published between 1990 and 2023 were included. Adding manual therapy to an active control resulted in short-term improvements in passive cervical rotation (odds ratio (OR) 9.79, 95%CI 4.26,22.50), passive cervical lateroflexion (OR 2.66, 95%CI 1.17,6.04), active cervical rotation (OR 3.94, 95%CI 1.08,14.35), symmetric head posture (OR 4.55, 95%CI 2.57,8.05), sternocleidomastoid tumour thickness (mean difference (MD) -2.12 mm, 95%CI -2.98,-1.26) and development of symmetrical movement (standardised MD -0.70, 95%CI -0.95,-0.45). The addition of an electrophysical agent to an active control reduced sternocleidomastoid tumour thickness (MD -2.03 mm, 95%CI -2.67,-1.39) and optimised Tuina reduced tumour thickness more than traditional Tuina (MD -1.20 mm, 95%CI -1.80,-0.59). Adverse events were uncommon but poorly reported, with 71 (71%) of studies providing no data. Study heterogeneity limited pooling of data for meta-analysis, and there was very low to low certainty evidence for all results, due to high risk of bias, small sample sizes and study heterogeneity. CONCLUSIONS: This review found that non-surgical, non-pharmacological treatments may be effective for CMT, but the certainty of evidence is very low to low. These findings are important in informing clinical guidelines and management for CMT and highlight an urgent need for large definitive trials that address the limitations of current studies. PROTOCOL REGISTRATION: Cochrane Database of Systematic Reviews (No.: CD012987).

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)0.013
metaresearch head score (Gemma)0.032
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.032
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.044
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
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 head0.364
Teacher spread0.336 · 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
Published2025
Admission routes2
Has abstractyes

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