STK1p Based on the Serum Thymidine Kinase 1 as a Tumour Proliferation Marker Detected for Risk Assessment of Pre-carcinoma to Carcinoma Colorectal Progression: A Meta-Analysis
Bibliographic record
Abstract
This study aimed at investigating whether serum thymidine kinase 1 concentration (STK1p) based on the TK1-IgY-pAb to assess the progression risk from colorectal adenoma polyp/dysplasia to colorectal carcinoma (CRC). A total of 25 publications containing patients with CRC (n=2,251), patients with colorectal polyp/dysplasia (n=1,165) and tumour-free controls (n=1,887) were analysed in the present meta-analysis. The publications were collected from PubMed, Embase, CENTRAL, CNKI, WanFang, VIP and SinoMed databases from January 1, 2009, until January 31, 2022. Articles were analysed using fixed or random effect models to calculate the mean difference. The Newcastle-Ottawa Scale was used for assessing the quality of collected studies. The meta-analysis followed the PRISMA statement. The results revealed that STK1p significantly distinguished tumour-free individuals from patients with CRC, and from patients with colorectal adenoma polyp/dysplasia (p<0.0001). Meanwhile, STK1p levels decreased by 34.1% within one month following surgery in CRC patients (p<0.0001). No significant publication bias was identified in this study. It was concluded that TK1-IgY-pAb is a reliable biomarker for early detection of colorectal adenoma polyp/dysplasia or pre-cancerous lesions, which may therefore prevent progression into colorectal carcinoma and give the patient a best chance of cure. Combining STK1p with colorectal-associated biomarkers, in addition to the determination of tumour stage and grade may therefore be of use.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".