Hangman’s Fracture
Bibliographic record
Abstract
In Brief Study Design. In vitro biomechanical flexibility experiment studying 5 sequential conditions. Objective. To determine the biomechanical differences among 3 fixation techniques after a simulated hangman’s fracture. Summary of Background Data. Type II hangman’s fractures are often treated surgically with a C2–C3 anterior cervical discectomy, fusion, and plating. Other techniques include direct fixation with C2 pars interarticularis screws or posterior C2–C3 fixation connecting C2 pars screws to C3 lateral mass screws. Methods. Seven cadaveric specimens (Oc–C4) were tested intact, after a simulated hangman’s fracture, and after each fixation technique. Flexion, extension, lateral bending, and axial rotation were induced using nonconstraining torques while recording angular motions stereophotogrammetrically. Results. Direct screw fixation reduced motion an average of 61% ± 13% during lateral bending and axial rotation compared to the injured state (P < 0.007). However, instability remained during flexion and extension. Posterior C2–C3 rod fixation provided significantly greater rigidity than anterior plate fixation during lateral bending (P < 0.008) and axial rotation (P < 0.04). Conclusions. Direct fixation of the pars ineffectively limits flexion and extension after a Type II hangman’s fracture. If pars screw fixation can be achieved, posterior C2–C3 fixation more effectively stabilizes a hangman’s fracture than anterior cervical plating. Type II hangman’s fracture was simulated in vitro. Three methods of fixating the injury were compared biomechanically: direct C2 pars fixation, anterior C2–C3 plating, and posterior C2 pars screws connected to C3 lateral mass screws. Direct pars fixation restored moderate stability, and posterior fixation was more rigid than anterior fixation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".