Comparing assessment methods of low back pain related disability in student circus artists: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Student circus artists put constant stress on their back. However, the presence of low back pain (LBP) and related disability in this population remains unclear. OBJECTIVES: To 1) examine LBP history in circus artists and compare related disability scores using the Oswestry Disability Index (ODI) and the Athlete Disability Index (ADI), and 2) examine the correlation between LBP-related disability scores, pain intensity and pain catastrophizing. METHODS: Thirty-three circus students completed an online survey on demographics, training history, and LBP. Participants reporting LBP filled the ODI, ADI, numerical pain rating scale (NPRS), and Pain Catastrophizing Scale (PCS). Descriptive statistics and Pearson's correlation coefficients were used to assess the correlations between the ODI, ADI, NPRS, and PCS. RESULTS: There was a significant positive correlation between the ODI and ADI (r= 0.77, p< 0.001) and between the NPRS and ADI (r= 0.52, p= 0.03), but no correlation between NPRS and ODI. While the PCS scores were significantly correlated with the NPRS ((r= 0.71; p< 0.001) and the ADI (r= 0.51; p= 0.032), no correlation was observed between the PCS and ODI scores (p= 0.088). Based on the ODI scores, 94.44% of the artists reporting LBP were classified with mild disability, 5.56% moderate, and 0% severe disability as compared to 66.67%, 27.78% and 5.55% with the ADI, respectively. CONCLUSION: Our study highlights the potential of the ADI as an effective tool for assessing LBP-related disability in circus artists, supported by a strong correlation with the NPRS.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".