Ten myths of back pain in older adults that can lead to ineffective and harmful care
Bibliographic record
Abstract
Low back pain (LBP) is one of the most disabling conditions in older adults and among the costliest in terms of healthcare expenditures. Many factors contribute to the disability and high costs of LBP in older adults, but one of the most preventable is the spread of misinformation and unhelpful attitudes, beliefs, and behaviors. These are often perpetuated by family, friends, social media, pharmaceutical companies, other industries, and healthcare providers. Myths about back pain foster false attitudes, beliefs, and behaviors that lead to inappropriate, costly, and sometimes harmful treatments. Such myths can result in psychological consequences, including fear of movement, poor self-efficacy, low motivation, anxiety, stress, and depression- all of which further perpetuate disability. Injections, surgeries, and medications for non-specific LBP are usually ineffective and are associated with significant side effects in older adults. The purpose of this paper is to dispel ten common myths of LBP in older adults, with the goals of changing attitudes, beliefs, and behaviors to reflect a more positive and evidence-based approach among practitioners and public. The aim is also to motivate practitioners to educate their older patients based on the best available evidence. This can improve outcomes, reduce costs, reduce disability, and improve quality of life among older adults with back pain.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".