Coccyx fractures in patients with spinal cord injury.
Bibliographic record
Abstract
AIM: The aim of this study was to report whether coccyx fractures are present in spinal cord injury (SCI) patients, who suffered from painful symptoms in the low back, gluteal, hip and thigh regions; and to determine the pain characteristics of coccydynia in these patients. METHODS: Twenty males and six females with traumatic SCI (mean age: 31.57+/-12.23 years) who described painful symptoms in the low back, hip, gluteal or thigh regions were included in the study. Pain assessment was done by using visual analogue scale (VAS) and the short form of McGill pain questionnaire. Radiological assessment comprised two-sided lumbar vertebrae, hip and coccyx X-ray graphics. RESULTS: Mean duration of SCI was 19.54+/-30.08 months. Nine patients (34.62%) had coccyx fractures. Sensory Pain Index and total McGill scores were found to be significantly higher in patients with coccyx fractures. Correlation analyses revealed significant correlations between VAS, SPI, affective pain index, present pain intensity and McGill total scores (all P<0.05). Duration of SCI was correlated with SPI (P=0.03) and total McGill scores (P=0.05). CONCLUSION: Coccyx fractures have been detected in patients with SCI and the presence of such fractures seems to affect the pain scenario unfavorably. Involved patients will be treated promptly and their rehabilitation process will be enhanced when these fractures are recognized early.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".