Diagnostic value of tumor marker pro-gastrin-releasing peptide in patients with small cell lung cancer: a systematic review.
Bibliographic record
Abstract
BACKGROUND: Lung cancer is one of the most common malignancies in the world and one of the leading cancers that result in death. The aim of this study was to evaluate and compare the diagnostic value of the serum tumor marker pro-gastrin-releasing peptide 31-98 (ProGRP31-98) to pathological diagnosis as reference standard in patients with suspected small cell lung cancer (SCLC). METHODS: Literature searches covering 1978 through to 2009 were performed in Pubmed, OVID, MEDLINE, EMbase, Cancerlit, China National Knowledge Infrastructure (CNKI), and CBM using the key search words; 'small cell lung cancer', 'tumor marker', 'ProGRP31-98' and 'diagnostic tests', 'ELISA', 'EIA' and 'diagnostic accuracy'. Studies were collected and data analyzed to evaluate the diagnostic value of serum ProGRP31-98 levels for the diagnosis of SCLC compared with pathology. Eligibility criteria for inclusion in the analysis were based on criteria for diagnostic research published by the Cochrane Screening and Diagnostic Tests METHODS: Group (SDTMG). The characteristics of the included articles were appraised and the data were extracted from the original articles for further statistical analysis of study heterogeneity using Review Manager 4.2 software. Based on study heterogeneity analysis, a suitable 'effect' model was selected to calculate pooled sensitivity and specificity by meta-analysis. A Summary Receiver Operating Characteristic (SROC) curve and the area under the curve (AUC) were generated and sensitivity analysis conducted. RESULTS: A total of 22 articles were entered into this meta-review, including 11 English articles with a quality at level C. In total, the studies involved 6759 subjects, of which 1470 were diagnosed with SCLC by pathology, and 5289 subjects diagnosed with non-SCLC (NSCLC). The meta-analysis showed that heterogeneity among studies was high (P = 0.00001, I(2) = 86.8%). With ELISA, the pooled sensitivity was 0.72 (0.70 to 0.75 at 95%CI) and the pooled specificity was 0.93 (0.92 to 0.94 at 95%CI); the SROC and the AUC were 0.8817. These data suggest that ProGRP31-98 has a relatively high rate of missed diagnosis (28%), but a relatively low rate of misdiagnosis (7%). CONCLUSION: From meta-analysis, we concluded that serum ProGRP31-98 is a valuable marker with a high specificity for diagnosis of SCLC with a similar diagnostic accuracy to pathology.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 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".