Neck pain in children: a retrospective case series.
Bibliographic record
Abstract
INTRODUCTION: Spinal pain in the paediatric population is a significant health issue, with an increasing prevalence as they age. Paediatric patients attend for chiropractor care for spinal pain, yet, there is a paucity of quality evidence to guide the practitioner with respect to appropriate care planning. METHODS: A retrospective chart review was used to describe chiropractic management of paediatric neck pain. Two researchers abstracted data from 50 clinical files that met inclusion criteria from a general practice chiropractic office in the Greater Toronto Area, Canada. Data were entered into SPSS 15 and descriptively analyzed. RESULTS: Fifty paediatric neck pain patient files were analysed. Patients' age ranged between 6 and 18 years (mean 13 years). Most (98%) were diagnosed with Grade I-II mechanical neck pain. Treatment frequency averaged 5 visits over 19 days; with spinal manipulative therapy used in 96% of patients. Significant improvement was recorded in 96% of the files. No adverse events were documented. CONCLUSION: Paediatric mechanical neck pain appears to be successfully managed by chiropractic care. Spinal manipulative therapy appears to benefit paediatric mechanical neck pain resulting from day-today activities with no reported serious adverse events. Results can be used to inform clinical trials assessing effectiveness of manual therapy in managing paediatric mechanical neck pain.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".