Bibliographic record
Abstract
STUDY DESIGN: A retrospective review of the literature. OBJECTIVE: This work serves as a comprehensive update of cervical arthroplasty. SUMMARY OF BACKGROUND DATA: Cervical arthroplasty has developed as a means to preserve normal spinal motion after an anterior cervical discectomy. Preserving motion may lead to an acute improvement in patient outcome and may decrease the incidence of symptomatic adjacent segment disease in the long-term. METHODS: The literature concerning the outcomes following anterior cervical decompression and fusion, the indications for cervical arthroplasty, the indications and contraindications for arthroplasty, the surgical technique, and early outcome studies for those devices currently in U.S. FDA IDE trials are reviewed. RESULTS: The most data are available for the Prestige, Bryan, and ProDisc-C devices. While these devices all preserve normal segmental motion, the articulations vary (metal on metal, metal on polyurethane, and metal on ultra-high molecular weight polyethylene). Wear testing indicates that these devices will have a long life once implanted. Preliminary outcomes compare very favorably to anterior decompression and arthrodesis. CONCLUSIONS: Cervical arthroplasty is a promising new technology that may improve patient outcome following anterior cervical decompression.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".