Bibliographic record
Abstract
PURPOSE: To provide a critical assessment of current stem-cell based pre-clinical models of astrocytomas (gliomas). METHODS: Data were archived from MEDLINE using Boolean formatted keyword queries. Top articles were selected for critical analyses depending on the qualitative assessment of the citation index, novelty of the findings, reputation of the research group and relevance to stem-cell based pre-clinical models of astrocytomas. RESULTS: The emergence of stem-cell based pre-clinical models of gliomas offers advantages for cellular transformation studies over other current in-vitro cell cultured based models. Cells utilized in these stem-cell based pre-clinical models are easier to transform, with the induced tumours demonstrating very high molecular and pathological recapitulations of astrocytomas that are derived from humans. These stem-cell based models fall into two categories. In the first, synthetic astrocytes can be differentiated from various stem cell sources such as the nervous system and embryos, and utilized in elegant forward genetic strategies to develop novel astrocytoma pre-clinical models. The second category represents a cancer stem cell pre-clinical model. In this model, glioma stem cells exhibit very high pathological recapitulations of the human tumours, and can be very informative to comprehend the basis of radio-chemoresistance among patients. CONCLUSION: The quest to develop robust pre-clinical models of astrocytomas is on an ongoing basis. The models are of clinical importance for the discovery of effective treatment modalities that can considerably improve the health of patients with this deadly disease.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".