Endoglin (CD105) as a putative prognostic biomarker for colorectal cancer: a systematic review
Bibliographic record
Abstract
The outcome of colorectal cancer (CRC) can be improved by the identification of prognostic biomarkers. This systematic review of observational cohort and case-control studies was conducted to investigate the role of Endoglin (CD105) in the prognosis of CRC. The databases PubMed, Web of Science, Scopus, and Cochrane CENTRAL were searched to identify the qualified studies using the relevant keywords. After the removal of duplicate articles, the screening was implemented on the titles, abstracts, and potential full-text articles. Afterward, the eligible cohort and case-control studies were identified, and the data were extracted into an Excel datasheet. In total, 11 observational cohort studies and 1 case-control study were identified to be eligible for this systematic review. The majority of the included studies achieved a moderate to high-degree quality according to the Newcastle-Ottawa Scale. Moreover, the eligible studies included a total of 1,400 patients with CRC and mean age of 60 years, the majority of whom were male. Endoglin was observed to be more upregulated in colorectal carcinomas and associated with poor survival outcomes, compared to healthy controls. The levels of Endoglin seem to reflect the degree of cancer invasiveness, therefore predicting dismal prognosis in patients with CRC. Larger and well-designed clinical studies with longer follow-up intervals are needed to investigate the role of Endoglin and its association with cancer metastasis.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".