Optimization of surgical fixation in cervical spine fractures using advanced imaging techniques: a systematic review of functional and neurological outcomes
Bibliographic record
Abstract
Cervical spine fractures are high-stakes injuries with substantial risks of permanent neurological damage and disability. Traditional imaging methods, including plain radiographs and fluoroscopy, are limited by low sensitivity and spatial resolution. This systematic review assesses the impact of advanced imaging specifically preoperative MRI, CT and intraoperative navigation systems on surgical fixation accuracy and patient outcomes. In methodology, we followed PRISMA guidelines, a comprehensive literature search was conducted across PubMed, Scopus and Web of Science from 2010 to 2024. Eligible studies included adult patients with cervical spine trauma undergoing surgical fixation with reported outcomes in screw accuracy, neurological recovery (ASIA scores) or functional status (JOA, NDI, SF-36). Data were synthesized and quality assessed using the Newcastle-Ottawa Scale. In results, eleven studies (n=1,220 patients) met inclusion criteria. Intraoperative CT-based navigation consistently improved screw accuracy (up to 98.1%), reduced malposition and operative times and minimized radiation to staff. MRI influenced surgical decision-making in elderly and neurologically impaired patients, particularly by identifying occult cord compression and reducing surgical delay. Select studies reported functional gains, including ODI improvements from 67.1% to 25.6% and VAS pain reduction from 8.2 to 2.2. Advanced imaging modalities significantly enhance surgical precision and contribute to improved patient safety and recovery in cervical spine trauma. Their integration into surgical planning supports evidence-based, patient-centered care, especially in high-risk or anatomically complex cases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".