Prognostic significance of matrix metalloproteinase 9 expression in osteosarcoma
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinase 9 (MMP-9) is significant in the progression of osteosarcoma (OS) via increasing tumor growth, invasion and metastasis. Although previous reports indicate the prognostic value of MMP-9 in OS, there is still a great degree on inconsistency between studies. Here we report a comprehensive evaluation of the value of MMP-9 in metastasis of OS by conducting a meta-analysis of published studies. METHODS: The quantity of the studies was evaluated using the Newcastle-Ottawa quality assessment scale (NOS). Sixteen studies with a total of 816 patients with OS were examined and we calculated the pooled odds ratio (OR) with corresponding 95% confidence interval (CI) (95% CI) to evaluate that the positive expression of MMP-9 predicts neoplasm metastasis and poor survival in OS. RESULTS: The results of Meta-analysis indicated that patients with positive expression of MMP-9 were significantly associated with neoplasm metastasis (OR = 4.69, 95% CI: 3.05-7.21, P <.001) and poor survival in OS with the pooled OR of 7.19 (95% CI 4.32-11.98, P <.001) when compared to their counterparts with a negative expression of MMP-9. The results of sensitivity analysis showed that the pooled OR was stable. It doesn't significantly change when a single study was removed. CONCLUSIONS: The results of meta-analysis indicated that MMP-9 may be a prognostic biomarker guiding the clinical therapy for OS.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".