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Interobserver and Intraobserver Reliability of Maximum Canal Compromise and Spinal Cord Compression for Evaluation of Acute Traumatic Cervical Spinal Cord Injury

2006· article· en· W2072566592 on OpenAlexaff
Michael G. Fehlings, Julio C. Furlan, Eric M. Massicotte, Paul Arnold, Bizhan Aarabi, James Harrop, D Greg Anderson, Christopher M. Bono, Marcel F. Dvorak, Charles Fisher, John France, Rune Hedlund, Ignacio Madrazo, Russ Nockels, Raja Rampersaud, Glenn R. Rechtine, Alexander R. Vaccaro

Bibliographic record

VenueSpine · 2006
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver General HospitalToronto Western HospitalUniversity of TorontoUniversity Health NetworkSt. Michael's Hospital
Fundersnot available
KeywordsMedicineSagittal planeRadiologyReliability (semiconductor)Spinal cord injuryCordSpinal cordSpinal canalSpinal cord compressionPhysical therapySurgery

Abstract

fetched live from OpenAlex

STUDY DESIGN: Prospective, blinded validation study of an objective, quantitative measure to assess maximum canal compromise (MCC) and maximum spinal cord compression (MSCC) in individuals with acute cervical spinal cord injury (SCI). OBJECTIVE: To examine the intraobserver and interobserver reliability of MCC and MSCC in individuals with acute traumatic cervical SCI. SUMMARY OF BACKGROUND DATA: To date, few quantitative reliable radiologic methods for assessing the extent of spinal cord compression in the setting of acute SCI have been reported. MCC and MSCC, as assessed on mid-sagittal CT and T2-weighted MR images, respectively, appear to have potential clinical and prognostic value. To date, the validation of these assessment tools has been limited to a small number of observers at a single institution. However, to date no study has focused on the reliability of these radiologic parameters among a large cohort of spine surgeons from North America and abroad. This type of validation is critical to allow the broader use of these outcome measures in research studies and in clinical practice. METHODS: Mid-sagittal MRI and CT images of cervical spine were selected from 10 individuals with acute traumatic cervical SCI. A total of 28 spine surgeons independently estimated CT MCC, T1-weighted MRI MCC, and T2-weighted MRI MSCC on two occasions using a calibrated ruler. In the first round of measurements, the observers estimated the radiologic parameters using only written instructions. The second measurement set was obtained after an interactive teaching session on the methodology. The order of the images was altered for the second set of measurements. RESULTS: Analysis using parametric and nonparametric statistics indicated high intraobserver reliability for CT MCC, T1-weighted MRI MCC, and T2-weighted MSCC with interclass correlation coefficients (ICCs) of 0.92, 0.95, and 0.97, respectively. The interobserver reliability for all three radiologic parameters was considered moderate with ICCs ranging from 0.35 to 0.56. CONCLUSION: Our results indicate that the intraobserver reliability for the MCC and MSCC was high. Although the interobserver reliability for all three radiologic parameters in the present study was below 0.75, the observed differences were small and largely accounted for by the limitations in the precision of the calibrated ruler. For cases with minimal cord compression, the measurement of canal stenosis (MCC) proved more accurate. In contrast, in cases with severe cord compression, the assessment of MSCC was more accurate. It is anticipated that the use of digital imaging technologies will further enhance the precision of these outcome measures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.372
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations88
Published2006
Admission routes1
Has abstractyes

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