Causes and Mechanisms of Common Coccydynia
Bibliographic record
Abstract
STUDY DESIGN: A total of 208 consecutive coccydynia patients were examined with the same clinical and radiologic protocol. OBJECTIVES: To study radiographic coccygeal lesions in the sitting position, to elucidate the influence of body mass index on the different lesions, and to establish the effect of coccygeal trauma. SUMMARY OF BACKGROUND DATA: A protocol comparing standing radiographs and radiographs subsequently taken in the painful sitting position in coccydynia patients and in controls has shown two culprit lesions: posterior luxation and hypermobility. Obesity and a history of trauma have been identified as risk factors for luxation. METHODS: Dynamic radiographs were obtained. The body mass index was compared with the coccygeal angle of incidence, sagittal rotation of the pelvis when sitting down, and the presence and time of previous trauma. The patients with the newly described lesions were examined after an anesthetic block under fluoroscopic guidance. RESULTS: Two new coccygeal lesions are described (anterior luxation and spicules). Obesity was found to be a risk factor. The body mass index determines the way a subject sits down, and lesion patterns were different in obese, normal-weight, and thin patients (posterior luxation: 51%, 15.2%, 3.7%; hypermobility: 26.5%, 30.3%, 14.8%; spicules: 2%, 15.9%, 29.6%; normal: 16.3%, 32.6%, 48.1%, respectively; P < 0.0001). Trauma affected the type of lesion only if it was recent (<1 month before the onset of coccydynia), in which case the instability rate increased from 55.6% to 77.1%. Backward-moving coccyges were at greatest risk of trauma. CONCLUSIONS: This protocol allows identification of the culprit lesion in 69.2% of cases. The body mass index determines the causative lesion, as does trauma sustained within the month preceding the onset of the pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".