Is hamstring muscle shortness responsible for low back pain in healthcare professionals?
Bibliographic record
Abstract
Background/Aim: Low back pain (LBP) is a highly prevalent pathology affecting more than half of our population. The lumbar region inherently possesses a complex structure; therefore, dozens of causes for the clinical presentation of acute/chronic pain are present. We focused on the impact of hamstring shortness on LBP in healthcare workers/professionals who need to keep medical records and perform invasive procedures while traveling overwhelming distances in relatively small workplaces. Methods: Our research was designed as a cross-sectional study and was conducted at Beykent University Hospital from March to April 2022. Sixty-two otherwise healthy healthcare workers/professionals aged 25–45 (both male and female) volunteered. Two equal groups with and without LBP were created. Oswestry disability index, Roland–Morris score, Quebec LBP questionnaire, Visual Analog Scale (VAS), active/passive knee extension, sit and reach, and forward bending tests were performed in each group. The collected data were statistically analyzed (confidence interval [CI]=20%; P<0.05). Results: Active/passive knee extension, sit and reach, and toe touch tests were significantly related to Roland–Morris, Quebec, and Oswestry Disability Index questionnaires; thus hamstring muscle shortness was significantly related to chronic low back pain (P<0.05). Short hamstring muscle length could accurately reflect the lower test scores obtained by the female participants. Conclusion: Hamstring muscle shortness could explain a significant proportion of low back pain in healthcare professionals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".