Algorithm for torticollis diagnosis in children of younger age groups
Bibliographic record
Abstract
BACKGROUND: Torticollis is a common term for abnormal head or neck positions. Torticollis can be due to a wide variety of pathological processes, from relatively benign to life-threatening. This syndrome is of particular relevance in pediatric practice and is often underestimated at the primary care level. AIM: To analyze the data of domestic and foreign literature on the etiopathogenesis and clinical features of various types of torticollis in children and develop algorithms for the differential diagnosis of torticollis in children of younger age groups. MATERIALS AND METHODS: A literature search was conducted in the open information databases of eLIBRARY and Pubmed using the keywords and phrases: torticollis, congenital muscular torticollis, non-muscular torticollis, acquired torticollis, and neurogenic torticollis, without limiting the depth of retrospection. RESULTS: Based on the literature data generalization, the classification of torticollis and the key directions of its differential diagnosis are systematized in tabular form. The range of differential diagnosis of torticollis is quite wide and has its characteristics in newborns and children of the first years of life, contrary to older children. The most common is congenital muscular torticollis. Concurrently, non-muscular forms of torticollis in the aggregate are not uncommon, more often with a more serious etiology, and require careful examination. Based on the analyzed literature, differential algorithms for torticollis diagnosis in children of younger age groups have been compiled. CONCLUSIONS: Increasing the level of the knowledge of pediatric clinicians in the etiopathogenesis of torticollis syndrome will improve the efficiency of early diagnosis of dangerous diseases that lead to pathological head and neck positions in children.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".