Rigid Forceps Technique for Complex Inferior Vena Cava Filter Retrieval：a systematic review and meta-analysis
Bibliographic record
Abstract
Study designs to be included Randomisedcontrol trials(RCTs),case series,casecontrol series,cross sectional studies,cohort studies,prospective studies,retrospective studies. Eligibility criteriaThe inclusion criteria for this Meta-Analysis included published original articles reporting more than 5 patients in whom complex IVC filters were removal using rigid forceps technique. Information sourcesWe searched the available lireratures in Medline，Embase，Pubmed and Cochrane databases. Main outcome(s)The technical success rate; m a j o r m a i n c o m p l i c a t i o n s r a t e , m i n o r complications motality and motality are analysed. Quality assessment / Risk of bias analysisThe quality of included articles was assessed through the Newcastle-Ottawa guidelines. Strategy of data synthesisThe meta-analyses will be performed using bothrandom effects models and fixed effects the chi-square based Q test and quantified using l2 statistics.If I2 statistics were>50%,heterogeneity was considered to besignificant.The potential publication biaswas appraised primarily by a funnel plot.Anasymmetric plot suggests a likely publication bias.The funnel plot asymmetry was further evaluated using Egger's Test.NoteExpress and Excel software will aldctrrt.infstatistical software packages R(http:// www.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".