Sagittal Skin Contour of the Cervical Spine: Interexaminer and Intraexaminer Reliability of the Flexicurve Instrument
Bibliographic record
Abstract
OBJECTIVES: To evaluate reliability of a simple instrument, the flexicurve, in determining cervical sagittal skin contour. METHODS: This study obtained repeated random measurements involving 3 investigators and 30 subjects once per day over a 2-day trial period. Thirty normal subjects were examined for cervical spine skin contour twice by 3 separate investigators with a 1-day delay. With subjects in a neutral standing position, investigators placed the flexicurve on the posterior portion of the subject's neck from the external occipital protuberance to the vertebral prominens and traced the flexicurve shape onto paper. The tracings were divided into 6 equal arcs and digitized. Statistical computation was performed on the depth at 5 points, arc angle, and arc radius of curvature. Interexaminer and intraexaminer correlation coefficients (ICCs) were calculated to determine reliability. RESULTS: All interexaminer correlation coefficients were in the poor range (<0.40). For the arc radius, arc angle, depth at top one third, and depth at bottom two thirds, the intraexaminer correlation coefficients were in the poor range. For the 3 deepest depths, the intraexaminer correlation coefficients were in the fair range (0.4-0.50). CONCLUSION: The flexicurve showed marginal reliability with most (12/16) ICCs in the poor range (ICC <0.40) and 4 values in the fair range (0.4 < ICC < 0.5).
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".