Does back pain prevalence really decrease with increasing age? A systematic review
Bibliographic record
Abstract
BACKGROUND: It is believed that the prevalence of back pain decreases around the middle of the sixth decade. However, back pain is still among the most commonly reported symptoms in the elderly and osteoarthritis, disc degeneration, osteoporosis and spinal stenosis all increase with age. In light of this, it is difficult to understand why the prevalence of back pain would decrease with increasing age. OBJECTIVE: This study aimed at summarising the scientific evidence on the trends of back pain prevalence with age. METHODS: Population-based studies reporting the prevalence of back pain, including people aged 65 years and over, were systematically retrieved from several bibliographic databases. These were read and assessed by two reviewers, and papers retained ('good quality studies') were aggregated according to specific criteria. RESULTS: Good quality studies showed a large heterogeneity as to their methods and prevalence figures. No specific patterns were detected by country nor outcome measure. However, most studies that considered severe forms of back pain found an increase of prevalence with increasing age. The curvilinear association between age and back pain prevalence that is widely mentioned in the literature was found only for benign and mixed problems. CONCLUSIONS: The evidence concerning the association of back pain prevalence with age is more sparse than currently believed and this association seems to be modified by the severity of the problem. This knowledge could have important public health implications, as the proportion of older people will increase considerably in the coming years in most industrialised societies.
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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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".