Diagnosis and non-operative treatment of lumbar disc herniation
Bibliographic record
Abstract
Lumbar disc herniation (LDH) occurs when a lumbar disc undergoes pathologic. Its symptoms generally include pain in the lumbar region, nerve irritation, or weakness in lower extremities. LDH is contributing to increased disabilities and dysfunction in the population. Many approaches are effective in addressing the disc herniation and symptoms of LDH. This paper focuses on LDH and discusses its diagnosis tests and non-operative treatments. Results show that certain manual muscle tests such as the straight leg raise tests is deemed as a necessary procedure for diagnosis. In addition, MRI stands out in diagnosing LDH among other imaging tests. For patients who cannot be applied MRI, CT is the best substitute. Non-operative treatments, including physical exercise/therapy, lumbar traction, and medicine, are the most effective approaches and the first choice of most patients with LDH. Aside from the modern approach, many traditional Chinese medicines and manipulations, including ACU, Warm needle moxibustion, Tuina, and Traditional Chinese Herbal Medicine, have favorable efficiency in treating LDH and are recognized by the world.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".