A Cohort Study Protocol of Low Back Pain In Rural Area Inhabitants: Fasa Low Back Pain Cohort Study (FABPACS)
Bibliographic record
Abstract
Background: Low back pain (LBP) is one of the main causes of disability in most societies that imposes much cost to the economic and health systems. In Iranian population, a prevalence as high as 27% has been reported for chronic LBP. So this study was designed to investigate the factors associated with low back pain in Iranian population. Methods/ Design: In Fasa Cohort Study, a branch of Persian cohort study, LBP patients were registered among the participants. A total of 10000 peoples, 1700 patients enrolled in cohort study were registered as LBP patients. In addition to detailed demographic, socioeconomic, anthropometric, nutrition, and medical history, limited physical examinations, determination of physical activity and body composition that was obtained in the cohort study, history of LBP, assessment of the pain severity, McGill pain inventory, and Oswestry questionnaire was filled for the LBP patients. All data are stored online using a dedicated software. Discussion: The cohort study is the best way to collect the necessary information required for policy making in the field of LBP. This study will help in providing some information about LBP in our area to establish a better management of the disease. Moreover, this study will provide many opportunities for clinical trials in this field, and we are going to do interventional studies in the cohort in future.[GMJ.2016;5(4):225-29]
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.015 | 0.003 |
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".