Factors predictive of topographical accuracy in spine level localization
Bibliographic record
Abstract
BACKGROUND: Pre-operative spine level localization by palpation of anatomical landmarks (ribs, spinous processes) in posterior approaches for surgeries from T4 to L2 is often inaccurate. This can lead to ineffective utilization of procedural time, increased radiation dose, potentially longer skin incision and wrong level surgery. Factors affecting topographical accuracy includes body mass index (BMI) of the patient, congenital or acquired deformity and knowledge of topographical anatomy. METHODS: (Vancouver, BC, Canada) and verification using an anterior-posterior radiograph. Potential factors predictive of accurate pre-operative spine level localization such as age, gender, BMI, palpable deformity, pathology related interspinous distance (ISPD) and pathology related skin to spinous process distance were evaluated. RESULTS: A prospective study was performed with 30 consecutive patients undergoing posterior spine surgery (T4 to L2). Accuracy of pathology related spine level localization using anatomical landmarks was only 40%. Pathology related ISPDs of more than 10 mm and palpable deformity was significantly correlated with successful determination of spine levels using anatomical landmarks. CONCLUSIONS: This study showed that poor spine level localization using anatomical landmarks was associated with pathology related ISPDs of less than 10 mm. Conversely, patients with palpable spinal deformity have their levels easily localized.
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 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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".