Assessment of Impact of Long-Cassette Standing X-Rays on Surgical Planning for Cervical Pathology
Bibliographic record
Abstract
BACKGROUND: Understanding the role of regional segments of the spine in maintaining global balance has garnered significant attention recently. Long-cassette radiographs (LCR) are necessary to evaluate global spinopelvic alignment. However, it is unclear how LCRs impact operative decision-making for cervical spine pathology. OBJECTIVE: To evaluate whether the addition of LCRs results in changes to respondents' operative plans compared to standard imaging of the involved cervical spine in an international survey of spine surgeons. METHODS: Fifteen cases (5 control cases with normal and 10 test cases with abnormal global alignment) of cervical pathology were presented online with a vignette and cervical imaging. Surgeons were asked to select a surgical plan from 6 options, ranging from the least (1 point) to most (6 points) extensive. Cases were then reordered and presented again with LCRs and the same surgical plan question. RESULTS: One hundred fifty-seven surgeons completed the survey, of which 79% were spine fellowship trained. The mean response scores for surgical plan increased from 3.28 to 4.0 (P = .003) for test cases with the addition of LCRs. However, no significant changes (P = .10) were identified for the control cases. In 4 of the test cases with significant mid thoracic kyphosis, 29% of participants opted for the more extensive surgical options of extension to the mid and lower thoracic spine when they were provided with cervical imaging only, which significantly increased to 58.3% upon addition of LCRs. CONCLUSION: In planning for cervical spine surgery, surgeons should maintain a low threshold for obtaining LCRs to assess global spinopelvic alignment.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".