The Risk Factors of Chronic Low Backache in Patients Presenting to a Tertiary Care Hospital of Pakistan
Bibliographic record
Abstract
Objective: To identify the risk factors of chronic low backache in patients presenting to a tertiary care hospital of Pakistan. Study Design: Cross sectional study. Place and Duration of Study: Department of Rheumatology, Fuji Foundation Hospital, Rawalpindi Pakistan, from Nov 2018 to Apr 2019. Methodology: Patients of ages between 18-80 years of ages with mechanical low backache were selected excluding those with malignancy and inflammatory backache. Patient’s characteristics including gender, age, education, monthly income, smoking status, exercise, previous back trauma, spinal surgery, posture mostly adopted, sleeping material, body mass index (BMI), and co-morbidities were noted down. For assessment of disability and depression Quebec disability index and patient health questionnaire (PHQ9) were used. Results: This study included 155 patients with backache with mean age (in years) of 55.45 ± 10.772. Mean duration of backache was 4.78 ± 4.36 years. Most common risk factor for low backache was age >40 years present in 144 patients (92.9%). 136 patients (87.7%) were not doing regular physical exercise.62.5% patients (97) were uneducated and 90 patients (58%) had low income. 82 patients (52.9%) used soft sleeping material. By using Quebec disability index, 57 patients (36.7%) were classified as having severe disability. Mild depression was present in 75 patients (48.3%) when assessed on PHQ-9 scale. Conclusion: Back pain was caused by many factors. Lack of regular exercise and education, use of soft sleeping material and in appropriate sitting posture can be addressed by education of the patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".