The Newcastle-Ottawa Scale quality assessment.
Bibliographic record
Abstract
<div> Objective This study investigated the relationship between the long non-coding RNA Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) expression and colorectal cancer (CRC) using a thorough systematic review and meta-analysis. Methods Under the PRISMA guidelines, a systematic review was conducted on studies published from the databases’ inception to September 18, 2023. Prognostic value and diagnostic accuracy were explored. Additionally, the association between levels of MALAT1 expression and pathological features was investigated. The statistical analysis was performed using the “meta” package of R. Results Among the pathological parameters examined, based on three studies involving 51 cases of metastatic CRC and 135 cases of non-metastatic CRC, a statistically significant correlation was found between the expression level of MALAT1 and distant metastasis, with an OR of 16.0118 (95% CI: 4.5618–56.2015). Three studies involving 378 cases reported overall survival and had a pooled HR of 2.3854 (95% CI: 1.3272–4.2875). Three studies involving 436 cases reported disease-free survival and had a pooled HR of 2.4772 (95% CI: 1.3774–4.4549). All prognosis studies utilized tumor tissue samples as specimens to assess the expression level of MALAT1. Case-to-control diagnostic studies with 126 cases and 126 controls had a pooled AUC value of 0.6173 (95% CI: 0.5436–0.6909), a pooled sensitivity of 0.675 (95% CI: 0.324–0.900), and a pooled specificity of 0.771 (95% CI: 0.685–0.839). Conclusions The expression of MALAT1 in CRC is highly correlated with distant metastasis and has an impact on survival and prognosis. MALAT1 could also be employed as a diagnostic biomarker. More prospective studies should be performed to assess the MALAT1 diagnostic potential in the early stages of CRC. </div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.218 | 0.042 |
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; both teacher heads agree on what is shown here.
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".