Considérer la statique des pieds plats comme facteur de risque de lombalgie non spécifique : une revue de littérature systématique
Bibliographic record
Abstract
Context : Nowadays, non-specific low back pain (LBP) is one of the ten most frequent motives of consultations. Its causes are multifactorial and often the result of mechanical constraints. The biomechanical relationship between variation in foot posture and the spine has been established. However, this link does not justify the clinical symptoms. The goal is to define the condition of flat feet (FF) as a risk factor for LBP and show the benefit of taking them into consideration in the management of the pathology. Method : PubMed, Science Direct and Cochrane Library databases were searched between 10/17/2023 and 01/15/2024. Articles were included if they’re observational studies whose population included men or women with LBP ; in which was evaluated the presence of FF and the association between the two factors, or the disability caused or the pain intensity. Internal validity and biases were assessed primarily with the Newcastle-Ottawa-Scale. Results : In total, five studies were included for 6148 participants including one cohort, three cross-sectional and one case series. The results are heterogeneous and do not justify the association between FF and the symptoms of LBP. Discussion : The results coupled with the biases do not allow us to implicate FF in the pathomechanism of LBP. Nevertheless, the low level of evidence of studies shows uncertain plausibility, making it impossible to confidently reject this risk factor in LBP treatment.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".