Percutaneous Image-guided Biopsy of the Spleen: Systematic Review and Meta-Analysis of the Complication Rate and Diagnostic Accuracy
Bibliographic record
Abstract
PURPOSE: To use meta-analysis to determine the complication rate and diagnostic accuracy of image-guided percutaneous needle biopsy of the spleen. MATERIALS AND METHODS: Several electronic databases were searched through July 2010 without language restrictions. Two reviewers independently selected studies that met the inclusion criteria for the diagnostic accuracy and complication rate arms of the study. Study data were independently extracted by the two reviewers. The primary 2 × 2 data were investigated with a random-effects meta-analysis of sensitivity and specificity. The complication rate data were investigated with a random-effects meta-analysis; sensitivity analysis of complication rate, excluding needles larger than 18 gauge, was performed. RESULTS: Four studies met the inclusion criteria for the diagnostic accuracy arm (639 patients), and nine met the inclusion criteria for the complication rate arm (741 patients). The meta-analysis showed a pooled sensitivity of 87.0% (95% confidence interval [CI]: 80.7%, 91.4%) and specificity of 96.4% (95% CI: 81.4%, 99.4%). The pooled major complication rate was 2.2% (95% CI: 0.8%, 5.6%). Sensitivity analysis with the removal of biopsies performed with needles larger than 18 gauge showed a major complication rate of 1.3% (95% CI: 0.6%, 2.5%). The most commonly encountered complications were hemorrhage followed by pain. CONCLUSION: Image-guided percutaneous biopsy of the spleen demonstrates high diagnostic accuracy and a major complication rate, for needles 18 gauge or smaller, that is similar to that reported for the liver and kidney. This technique should be considered a favorable alternative to splenectomy.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".