Reliability of biomechanical mensuration methods of the sagittal cervical spine on radiography used in clinical practice: A systematic review of literature
Bibliographic record
Abstract
Abstract Biomedical literature assessing reliability of mensuration of sagittal cervical spine alignment on radiographs is limited. This review aims to systematically identify and assess reliability studies on biomechanical assessments of the sagittal cervical spine used in clinical practice. The study design was registered with PROSPERO (CRD42023402990). Funding was from CBP, Non-Profit (Eagle, ID, USA). Inclusion criteria involved studies in English with: human subjects, radiography of the sagittal cervical spine, and reliability analysis on biomechanical mensuration of sagittal cervical spine. Exclusion criteria involved studies with: geometric modeling, animals, cadavers, phantom mannequins, and non-radiographic studies. Pubmed, CINAHL, AltHealth Watch, and Web of Science databases were searched from inception through January 24, 2023. The quality appraisal tool for studies of diagnostic reliability (QAREL) assessed bias risk. Results are presented following the synthesis without meta-analysis (SWiM) in systematic reviews guidelines. Scrutiny of inclusion criteria yielded 51 articles. Results are limited due to heterogeneity of the various mensuration methods and statistical analyses. The preponderance of evidence found good to excellent reliability. The results of this systematic review show that sagittal radiographic mensuration of cervical spine biomechanics and alignment is a reliable method for the diagnosis and management of spine conditions in clinical settings.
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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 | 0.029 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".