Low back pain rehabilitation in 2020: new frontiers and old limits of our understanding
Bibliographic record
Abstract
Low back pain (LBP) is the most common musculoskeletal condition affecting the quality of life of individuals, especially if persistent. Over the decades, a lot of work has been done in an attempt to reduce the negative impact of back pain, and help patients recover and maintain a better quality of life. New insights are coming from different fields of research, with a lot of work being done in searching for the etiology of LBP, describing the different phenotypes of symptomatic spines, and identifying factors involved in the persistence of the disease. Nevertheless, still a lot remains to be done to fully understand the problem of back pain and its causes. Even today, there appears to be a wide gap between basic science and applied rehabilitation research on LBP. The first is still searching in many different ways for the "holy grail" of the pain generator and providing very interesting results with particular relevance to surgical, drug-related and other biological approaches, while the second is pragmatically focusing on modifiable factors that may influence back pain outcomes. Yet, personalized, effective spine care has not been fully realized. While we recognize the potential of basic science advances, there is an immediate need for more translational rehabilitation research, as well as studies focused on the effectiveness of rehabilitation approaches.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".