Advances in the identification of molecular markers for bone neoplasia
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Bone tumors represent a heterogeneous and poorly understood group of neoplasms affecting patients of all ages. This review is intended to highlight recent advances in the identification of diagnostically relevant molecular biomarkers. AREAS COVERED IN THIS REVIEW: This review offers a summary of basic techniques in molecular pathology. In the case of primary bone tumors with diagnostically applicable molecular markers, an overview of the tumor is provided incorporating the germane background and advances in the identification of molecular markers. WHAT THE READER WILL GAIN: The reader will gain an understanding of the techniques governing the discovery of biomarkers, and their applicability in diagnostic bone pathology. TAKE HOME MESSAGE: Molecular analysis has identified key diagnostic biomarkers in only a small proportion of bone tumors. Many of these findings owe their existence to earlier karyotype-based cytogenetic studies. In cases where characteristic cytogenetic findings are absent, there remains a tremendous need to interrogate rigorously these lesions using emerging techniques such as whole genome sequencing. It is assumed that with a more precise understanding of the tumor genetic code, more accurate diagnostic, prognostic and therapeutic markers will emerge.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".