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Record W2273818943 · doi:10.4103/1673-5374.165508

Subaxial cervical spine injury classification system: is it most appropriate for classifying cervical injury?

2015· article· en· W2273818943 on OpenAlexaff
Rafael Martínez-Pérez, Francisco Fuentes, VíctorS Alemany

Bibliographic record

VenueNeural Regeneration Research · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCervical spineBluntCervical spine injurySpinal cord injuryCervical vertebraeSurgeryPhysical medicine and rehabilitationSpinal cord

Abstract

fetched live from OpenAlex

The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality (Jain et al., 2015). Neurological deterioration ranging from complete spinal cord injury (SCI) to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures (Alday, 1996). Few classifications (Holdsworth, 1970; White et al., 1975; Allen et al., 1982; Denis, 1984; Vaccaro et al., 2007) have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity. Classical systems: Allen classification has been commonly used over the past two decades. This system is based on a clinical review of 165 patients with blunt cervical trauma (Allen et al., 1982). Each lower cervical injury is divided into 6 categories of injury, which truly describe the attitude of the cervical spine at the time of injury and the dominant force vectors – compressive flexion, vertical compression, distractive flexion, compressive extension, distractive extension and lateral flexion. Distractive flexion injuries were the most common in these 165 patients. Within each category, a series of injury are described, ranging from mild to severe, which are related at the same time with neurological impairment. However, this classification does not allow to make a comparison, in terms of neurological outcome, between different categories of injury mechanisms. Allen system also fails to explain clearly some important force vectors, such as rotation and their implication in spine stability. Neurological status is not included as a criteria of this structural and mechanistic classification. Those individuals with SCI without radiological abnormalities (SCIWORA) are underrepresented and may lead to mistakes in terms of management and predicting clinical outcome, despite the potential disability in this subgroup of patients. White and Panjabi (1975) elucidated that similar injury mechanisms can produce different injury patterns due to the complexity of the specific forces, moments and positions. They described a complex point-based system to asses cervical spine stability. Not only clinical data, but also in vitro biomechanics testing are the basis of this classification. A street test is also required, which runs against the simplicity and applicability in critical patients. This system also fails in terms of validity and inter observer reliability. Two-column and three-column (Holdsworth, 1970; Denis, 1984) systems may provide more simplicity and a better understanding of the common injury patterns seen in the lower cervical spine. Holds worth in his two column model, postulated that the integrity of the posterior bony elements and the posterior ligamentous complex is the major determinant of stability. However, this scheme was insufficient to predict the presence of an unstable subset of compression fractures. Dennis modified the two column theory into a three column system. He defined a middle column, consisting the posterior longitudinal ligament and the posterior third of the vertebral body. The term of “unstable fracture” was coined when the middle column and one of the remaining columns – anterior or posterior – were injured. In spite of being primarily described to elucidate different patterns of fracture in thoracic and lumbar spine, its use has commonly widespread over scientific community. Lower cervical biomechanics differ so much with lumbar and dorsal spine, as C-spine implies wider range of mobility, lesser fixation and a different distribution of articular facets. Denis model (two-column) widely used, is an oversimplification that fails to incorporate the biomechanics importance of the spinal ligaments, which are also linked to degree of SCI (Martinez-Perez et al., 2014b). Moreover, some specific patterns, such as “chance fracture”, is underreported in the cervical spine. So, in our opinion, resorting to these nomenclature to define some cervical fractures may result misleading (Alday, 1996). Changes in paradigma: from the structure to the function: All of these “classical” system mentioned above are based on the mechanism of injury extracted from plain radiographs or CT scans, ignoring the contribution of ligaments to stability and the role of MRI in the stratification (Martinez-Perez et al., 2014a). The role of neurological impairment to determine the prognosis has been clearly demonstrated in previous works and represents an important indicator of severity of cervical spine injury (Miyanji et al., 2007). Moreover, neurological status may be the single most influential factor to indicate conservative or surgical management. Its widely accepted that incomplete neurological injuries requires surgical decompressive procedure, even in the case of absence of frank structural instability. Then, some authors considere that neurological impairment should be include in new systems of classification in order to give them the possibility to help to the surgeon in decision making (Moore et al., 2006; Vaccaro et al., 2007). The need for a practical lower cervical spine classification system directly linked to a clinical decision-making algorithm prompted the Spine Trauma Study Group to develop the Subaxial Cervical Spine Injury Classification (SLIC) system (Vaccaro et al., 2007). This is a severity scale that attempts to provide a utilitarian classification framework to the clinician and surgeon involved in the treatment of sub-axial injuries. Instead of building the system on an inferred mechanism, it is based on 3 components of injury (mechanism/morphology, integrity of the posterior ligamentous complex and neurological status of the patients, Table 1) which, by consensus, represent major and largely independent determinants of prognosis and management. The total number of points is calculated for each cervical fracture or dislocation based on these three major categories, an the final score is linked to an algorithm to help guide management: injuries with a SLIC score of 4 or less are managed conservatively, fractures with a score of 6 or more are surgically operated, and injuries with a score of 5 may be managed either with surgery or non operatively at the surgeon's discretion (Dvorak et al., 2007). In this way, the SLIC severity scale is the first sub-axial trauma classification system to abandon mechanism and anatomy characterized by other systems in favor of injury morphology and clinical status. However, this system lacks the attention toward the level of injury, which also can determinate either the prognosis, as the surgical approach in each case. Other limitation of the mentioned system is the current use in neurosurgical community, lower than older classifications (Chhabra et al., 2015).Table 1: The Subaxial Injury Classification and Severity Score System (SLIC)Despite of being far from an ideal classification for cervical trauma, by building the system on injury patterns less severe to more severe, the SLIC severity scale helps to objectify the optimal management in each case. Further studies have shown that SLIC scale exhibits excellent validity and inter observer reliability, unlike other classifications (Vaccaro et al., 2007; Patel et al., 2010; Aarabi et al., 2013). Conclusion: “Classical” cervical injury classifications are characterized for its complexity, low applicability, and its uselessness in guiding therapeutic options. New schemes, as SLIC system, includes determinant factors in prognosis, such as neurological impairment. It will hopefully facilitate the development of evidence-based guidelines that may influence other aspects of the therapeutic decision-making process (e.g., which operative approach is most appropriate for a particular injury). We certainly believe its accuracy and reproducibility will increase over time as surgeons become more familiar with the protocol.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.251
GPT teacher head0.460
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations9
Published2015
Admission routes1
Has abstractyes

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