Beneficial Intervertebral Disc and Muscle Adaptations in High-Volume Road Cyclists
Bibliographic record
Abstract
PURPOSE: Cycling is widely practiced as a mode of transportation, a leisurely pursuit, and a competitive sport. Approximately half of cyclists experience low back pain. Yet, there has been limited study of spine tissue adaptations due to cycling. METHODS: To investigate potential risk factors for spinal pain, we compared 18 high-volume cyclists (>150 km·wk for ≥5 yr) to 18 height-matched nonsporting referents. Participants had no history of spinal pathology. Magnetic resonance imaging was used to quantify intervertebral disc (IVD) morphology and hydration, and psoas, erector spinae, quadratus lumborum, and multifidus muscle size and fat content. Endurance of trunk muscles (flexors and extensors) was measured, and physical activity levels were assessed objectively using accelerometry. RESULTS: Cyclists' IVD showed prolonged T2 time (+10.0% ± 17.3%; P = 0.021), implying better IVD hydration and glycosaminoglycan content, compared with referents. Lower thoracic and upper lumbar IVD T2 time were longer in cyclists (P ≤ 0.029) but not at the lower lumbar spine. T2 time differences were larger in the nucleus pulposus compared with the annulus fibrosus. Cyclists showed larger psoas muscles with less fat content compared with referents. Cyclists also exhibited longer isometric trunk endurance times (P ≤ 0.036) and higher physical activity levels (osteogenic index, P = 0.038). CONCLUSION: Despite previous studies reporting higher than average prevalence of back pain in cyclists, the high-volume road cyclists in our cohort showed no anatomical or functional deficiency in spinal structures. By contrast, we found evidence for beneficial adaptations to the IVD and psoas muscles in high-volume cyclists compared with referents. These data support the notion that cycling is not detrimental to the spine, rather, by contrast, may be associated with beneficial changes at the spine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".