Methodology and cohort profile for the Hangzhou Lumbar Spine Study: a study focusing on back health in a Chinese population
Bibliographic record
Abstract
Back pain is a worldwide health problem, adding a tremendous burden to modern societies. However, little information on back health is available in China, even though a quarter of the world's population is Chinese. To enhance knowledge in this area, we designed and initiated the Hangzhou Lumbar Spine Study, which is a cross-sectional study of a general sample of mainland Chinese with focusing on disc degeneration, Modic changes, endplate lesions, and back pain. The study consists of a structured questionnaire to measure back pain history and lifetime exposure to suspected risk factors, magnetic resonance imaging of the lumbar spine, bone mineral density study of the spine and hip, and DNA sample analysis. Here we briefly introduce the study methodology, report the test-retest reliability of the questionnaire, and describe the cohort profile to date. Since May 2014, 301 randomly selected subjects (male/female, 122/179; mean age, 51.0 years; range, 20-87 years) have been recruited. Tests-retests of the questionnaire, completed by 40 participants, revealed good reliability. To our knowledge, the Hangzhou Lumbar Spine Study is the first population-based epidemiological study conducted to characterize lumbar spinal phenotypes and back pain, their interaction, and their associations with lifetime environmental exposure, in mainland Chinese. Epidemiological information obtained from a reliable questionnaire, magnetic resonance (MR) imaging data, dual energy X-ray absorptiometry (DXA) measurements, and DNA analysis may serve as a valuable reference for future studies on back health, particularly for mainland Chinese.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".