Bibliographic record
Abstract
Low back pain (LBP) is one of the most common pathologies of the musculoskeletal system worldwide. The article presents data on the main causes of LBP (mechanical, non-mechanical and visceral), the importance of differential diagnosis concerning possible causal factors, taking into account the presence of so-called red and yellow flags. The article also notes the fact of increased pain in the lower back and neck revealed in a number of studies in the conditions of COVID-19 and lists the main possible causes of this phenomenon. The expediency of an integrated approach to patient management with acute and chronic LBP, given the principles of evidence-based medicine, is substantiated. Drug therapy means are characterized, and data are presented indicating complex drugs’ efficacy, including those based on NSAIDs with a proven analgesic effect (diclofenac) and B vitamins. The importance of maintaining the physical activity of patients with both acute and chronic LBP and the ability of some non-pharmacological pain therapy methods (manual therapy, Kinesio taping, etc.) to reduce pain and fear of movement are noted. KEYWORDS: low back pain, red flags, differential diagnosis, treatment, NSAIDs, B vitamins. FOR CITATION: Pizova N.V. Modern patient management with low back pain. Russian Medical Inquiry. 2021;5(10):659–667 (in Russ.). DOI: 10.32364/2587-6821-2021-5-10-659-667.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".