Spinal gout diagnosis in chiropractic practice: narrative review.
Bibliographic record
Abstract
Objective: To review and summarize the recent literature, increase awareness and provide guidance for chiropractic physicians regarding the diagnosis of spinal gout. Methods: A search of PubMed was undertaken for recent case reports, reviews and trials relating to spinal gout. Results: Our analysis of 38 cases of spinal gout revealed that 94% of spinal gout patients presented with back or neck pain, 86% displayed neurological symptoms, 72% had a history of gout, and 80% had raised serum uric acid levels. Seventy-six percent of cases proceeded to surgery. A combination of clinical findings, laboratory tests and appropriate utilization of Dual Energy Computed Tomography (DECT) has the potential to improve early diagnosis. Conclusion: Gout is an uncommon cause of spine pain; however, it must be considered in the differential diagnosis as outlined in this paper. Increased awareness of the signs of spinal gout and earlier detection and treatment has the potential to improve the quality of life of patients and reduce the need for surgery.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".