Agreement Between Orthopedic Surgeons and Neurosurgeons Regarding a New Algorithm for the Treatment of Thoracolumbar Injuries
Bibliographic record
Abstract
INTRODUCTION: Considerable variability exists in the management of thoracolumbar (TL) spine injuries. Although there are many influences, one significant factor may be the treating surgeon's specialty and training (ie, orthopedic surgery vs. neurosurgery). Our objective was to assess the agreement between spinal orthopedic and neurologic surgeons in rating the severity of TL spine injuries with a new treatment algorithm. This information could be important in establishing consensus-based protocols for managing these challenging injuries. METHODS: Twenty-eight spinal surgeons (8 neurosurgeons and 20 orthopedic surgeons) reviewed 56 TL injury case histories. Each case was classified and scored according to the TL injury severity score (TLISS). The case histories were reordered and the physicians repeated the exercise 3 months later. At both intervals the surgeons were asked if they agreed with the final treatment recommendation of the TLISS algorithm. The reliability and decision validity of the TLISS was compared. RESULTS: Between-group interrater reliability was similar to within group reliabilities. Intrarater reliability was also similar between groups. The between speciality interrater reliability of the TLISS management recommendation was moderate (74% agreement, kappa=0.532). Orthopedic and neurosurgeons agreed with the TLISS management recommendation 91.4% and 94.4% of the time, respectively. CONCLUSIONS: The TLISS demonstrated good reliability in terms of intraobserver and interobserver agreement on the algorithmic treatment recommendations. The recommendation for operation seems to be consistent between fellowship-trained orthopedic and neurosurgical spine surgeons. This type of classification system may reduce the existing variability and initial management decision for treatment of TL injuries.
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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 | 0.043 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".