Chiropractic Management of Lumbar Disc Herniation in a Patient With Co-existing Liver Cancer: A Case Report
Bibliographic record
Abstract
The current case report outlines the chiropractic management of a 30-year-old male construction worker who presented with symptoms of lumbar disc herniation with co-existing stage IV liver cancer. The patient reported experiencing substantial lower back pain and decreased sensation in his right leg following a fall at work, impacting his mobility and quality of life. The complexity of this case is underscored by the challenge of differentiating between pain due to metastatic disease and that related to the fall. The chiropractic treatment plan included gentle joint mobilization, instrument-assisted soft tissue mobilization, and low-impact exercises tailored to the patient's overall health status. The treatment protocol markedly improved pain levels, range of motion, and overall quality of life. This case highlights the potential role of chiropractic care in managing complex cases of lumbar disc herniation, even in the presence of severe illnesses such as liver cancer. This study provides valuable insights into the importance of personalized and adaptable treatment strategies in managing such cases, contributing a unique perspective to the scientific literature.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".