Treatment of Upper Crossed Syndrome: A Narrative Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Upper crossed syndrome (UCS) is a common musculoskeletal condition that is characterized by tightness and weakness of the muscles of the neck, shoulders, and upper back. The aim of this current study is to summarize and provide an overview of the treatment in patients with UCS. MATERIALS AND METHODS: A MEDLINE (PubMed), Cochrane library, Embase, Scopus, and Web of Science database search was conducted for English-language articles about upper crossed syndrome that were published until 19 January 2023. To identify potentially relevant articles, the following key search phrases were combined: "upper crossed syndrome", "upper cross syndrome", "diagnosis", and "treatment". A total of 233 articles were identified. After reading the titles and abstracts and assessing their eligibility based on the full-text articles, 11 articles were finally included in this review. The risk of bias (RoB) was assessed using RoB-2 and ROBINS-I for the randomized controlled trials (RCTs) and the non-randomized clinical trial (non-RCT), respectively. RESULTS: Among eleven studies that investigated the effect of treatment programs for UCS, five studies compared the therapeutic effect of exercise programs with controls, whereas six compared different rehabilitative treatment strategies, such as the muscle energy technique, soft-tissue mobilization, and stretching exercises. In addition, regarding the study design, ten studies were RCTs and only one study was a prospective observational study. CONCLUSIONS: Treatment programs including various types of exercises and techniques to correct an abnormal posture and restore neuromuscular imbalances are effective for decreasing pain and improving neck disabilities and postural deviations in patients with UCS.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".