Recognition and management of atlanto-occipital dislocation: improving survival from an often fatal condition.
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of atlanto-occipital dislocation and associated occipital condyle fracturcs so as to alert physicians to this rare injury and potentially improve patient outcome. The pertinent anatomy, mechanism of injury, clinical and radiologic evaluation and the management of these rare injuries are discussed in an attempt to alert physicians to this type of injury and to improve outcome. DATA SOURCES: The data were obtained from a MEDLINE search of the English literature from 1966 to 1999 and the experience of 4 spine surgeons at a quaternary care acute spinal cord injury unit. STUDY SELECTION: Detailed anatomic and epidemiologically sound radiology studies were identified and analyzed. Only small retrospective studies or case series were available in the literature. DATA EXTRACTION: Valid anatomic, biomechanical and radiologic evaluation was extracted from studies. Clinical data came from limited studies and expert opinion. DATA SYNTHESIS: Early diagnosis is essential and is facilitated by a detailed clinical examination and strict adherence to an imaging algorithm that includes CT and MRI scanning. When the dislocation is identified, timely gentle reduction and prompt stabilization throuigh nonoperative or operative means is found to optimize patient outcome. CONCLUSIONS: Atlanto-occipital dislocation should be suspected in any patient involved in a high speed motor vehicle or pedestrian collision. Once suspected, proper imaging and appropriate management of these once fatal injuries can improve survival and neurologic outcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".