Expression of HER-2 in surgical specimen and biopsy as a biomarker of metastasis in patients with osteosarcoma: a meta-analysis
Bibliographic record
Abstract
Background: Previous studies have evaluated the effect of human epidermal growth factor receptor 2 ( HER-2 ) expression on the metastasis of patients with osteosarcoma (OS) while the results remain conflicting. Here we performed a systematic review and meta-analysis to determine the value of HER-2 in prognosis of OS. Methods: A comprehensive search using NCBI PubMed, the Cochrane library, Embase, ISI Web of Knowledge, Springer, China National Knowledge Internet database (CNKI), Wanfang database, Chinese VIP database and Chinese Biological Medical Database (CBM) from inception through Aug 28, 2018 was conducted to investigate HER-2 expression and OS metastasis. We evaluated the quantity of the studies using Newcastle-Ottawa quality assessment scale (NOS). Results: There were 15 studies with 652 OS patients involved. The results of meta-analysis showed that positive expression of HER-2 was associated with OS metastasis (OR =4.42; 95% CI, 2.91–6.71; P Conclusions: The results of this study suggest that HER-2 positive expression indicates OS metastasis, while it’s needed to perform more prospective studies to confirm the prognostic value of HER-2 in patients with OS.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".