Cervical intradiscal pressure responses to end-range supine postures: a cadaveric investigation
Bibliographic record
Abstract
BACKGROUND: Cadaveric studies suggest neck postures may affect cervical intradiscal pressure (CIDP) and potentially contribute to intervertebral disc (IVD) pathologies. Despite neck flexion and protraction posture prevalence and potential impact on cervical IVD health, no studies have investigated CIDP during end-range protraction and retraction. This study investigated (1) CIDP differences between cervical traction, six sagittal plane cervical end-ranges, and neutral posture; (2) CIDP and segmental cervical range of motion (ROM) correlation; and (3) CIDP measurement reliability. METHODS: Seven cadaveric specimens, mean age 80.6±7.2 years, had cervical segmental ROM assessed by lateral radiographs and CIDP responses measured by fiberoptic pressure sensors in C4-5, C5-6, and C6-7 IVDs for supine end-range chin to neck, chin to sternum, protraction-flexion, occiput to neck, occiput to thorax, retraction-extension, and neutral traction. RESULTS: =0.82, p = .02. Reliability was good to excellent for CIDP and segmental ROM measurements (ICC > 0.92, 95%CI 0.86-0.98). CONCLUSIONS: Consistent chin to sternum increases and traction decreases in CIDP occurred at all cervical IVD levels. The CIDP tended to increase during flexion end-ranges at all IVD levels, while extension, protraction, and retraction tended to decrease at C5-6, C6-7 and increase at C4-5. Large positive or negative CIDP variations with even larger standard deviations were observed within and between cervical IVD segments during various postures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".