Bibliographic record
Abstract
One of the hallmarks of cancer is limitless proliferative capacity, which is tightly associated with the ability to maintain telomeres. Over the last decade, the telomere biology of pediatric cancers has begun to be elucidated. Most pediatric leukemias and embryonal solid tumors activate the enzyme telomerase, a specialized reverse transcriptase that adds nucleotide repeats to telomeres. In general, high levels of tumor telomerase expression are associated with unfavorable outcome, although results vary according to tumor type. Some pediatric tumors, including osteosarcoma and glioblastoma multiforme, lack telomerase activity and maintain telomeres via a recombination-based mechanism called ALT (alternative lengthening of telomeres). Telomerase is a highly attractive therapeutic target for pediatric cancer because the enzyme plays a key role in conferring cellular immortality, is present in most tumors, and is relatively specific for cancer cells. Telomerase inhibitors have been evaluated in preclinical models of adult cancers, but few studies have been conducted on pediatric cancers. Further research is required to define how telomere biology can be used to clinical advantage in malignancies of childhood.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".