Animal Assisted Therapy in a Special Needs Dental Practice: An Interprofessional Model for Anxiety Reduction
Bibliographic record
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths globally, with approximately 85% of cases caused by sporadic mutations. This reference research article focuses on the molecular aspects of early CRC detection in familial cases, particularly in hospitals in Medina. The study primarily reviews genetic markers, including APC, MLH1, and MSH2, which are associated with hereditary CRC syndromes like Lynch syndrome and familial adenomatous polyposis (FAP). However, hereditary cases represent only about 5% of all CRC cases, while familial cases make up 10-20%. The research also explores the significance of sporadic CRC, which constitutes the majority of cases and often occurs before the age of 50 due to somatic mutations. Advanced molecular screening techniques such as circulating tumor DNA (ctDNA) analysis, methylation-specific PCR (MSP), and miRNA assays are evaluated for their effectiveness in early CRC detection. These methods offer non-invasive and highly sensitive screening options, particularly for identifying CRC in high-risk individuals. Despite the focus on genetic mutations, the research underscores the importance of detecting sporadic mutations, as they are more prevalent in the population and pose a growing challenge. The findings highlight the need for localized screening programs in Medina that integrate molecular diagnostics for both hereditary and sporadic CRC. Recommendations include the implementation of genetic testing for high-risk families and further research into the genetic and environmental factors specific to the Medina population.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".