Molecular diagnosis of vertebral segmentation disorders in humans
Bibliographic record
Abstract
BACKGROUND: Vertebral malformations contribute substantially to the pathophysiology of kyphosis and scoliosis, common health problems associated with back and neck pain, disability, cosmetic disfigurement and functional distress. OBJECTIVE: To provide an overview of the current understanding of vertebral malformations, at both the clinical level and the molecular level, and factors that contribute to their occurrence. METHODS: The literature related to the following was reviewed: recent advances in the understanding of the molecular embryology underlying vertebral development and relevance to elucidation of etiologies of several known human vertebral malformation syndromes; outcomes of molecular studies elucidating genetic contributions to congenital and sporadic vertebral malformations; and complex interrelationships between genetic and environmental factors that contribute to the pathogenesis of isolated syndromic and non-syndromic congenital vertebral malformations. RESULTS/CONCLUSION: Expert opinions extend to discussion of the importance of establishing improved classification systems for vertebral malformation, future directions in molecular and genetic research approaches to vertebral malformation and translational value of research efforts to clinical management and genetic counseling of affected individuals and their families.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".