Psychometric properties and minimal clinically important difference of the World Health Organization disability assessment schedule in persons with low back pain: A systematic review
Bibliographic record
Abstract
measurement, a spinal health screening project was conducted among students aged 6-18 yrs in 64 regions of Yunnan, China (involving 380 schools: 293 primary and 87 middle).Taking ATR 5 as the positive standard to screen the population of suspected scoliosis so as to calculate the regional prevalence rate.The regional altitude was recorded respectively.The relationship between different altitudes and prevalence was analyzed with Pearson correlation and unary linear regression model.Results: The altitudes of the 64 regions spanned 1224m-2379m.A total of 3190 students (1423 males, 1767 females) with suspected scoliosis were detected and the overall prevalence was 2.3%.For the 64 regions, the mean prevalence of suspected scoliosis was 2.1% (0.2%-4.9%): 1.9% (0-7.4%) for male and 2.4% (0-5.6%) for female.Correlation analysis shown the prevalence of suspected scoliosis was correlated with altitude positively (r ¼ 0.484, R 2 ¼ 0.234, P < 0.01).Furthermore, positive correlation was also detected between the male/female prevalence and altitudes (P<0.01).(Figure 1) Conclusion: The prevalence of suspected scoliosis increased with altitude.Highaltitude region could face a higher risk of scoliosis.
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 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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".