[Clinical outcomes following microsurgery and endovascular embolization in the management of spinal dural arteriovenous fistula: A meta-analysis study].
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical effect of microsurgery and endovascular embolization in the treatment of spinal dural arteriovenous fistula (SDAVF) by meta-analysis. METHODS: A systematic review was performed to retrieve all relevant literature about surgical treatment or endovascular embolization of SDAVF up to December 2019 through PubMed, Embase, Web of Science, Cochrane Central Register of Controlled Trials Results, CNKI, Wanfang Data, and SinoMed. The Chinese and English key words included: "SDAVF", "spinal dural arteriovenous fistula", "spinal AVM", "spinal vascular malformation and treatment". The included studies were evaluated using the Newcastle-Ottawa scale. The early failure rate, long-term recurrence, neurological recovery, and complications were evaluated and the clinical effects of the two methods in the treatment of SDAVF were compared by using RevMan 5.3 software. And a further subgroup analysis of the therapeutic effect of endovascular embolization with different embolic agents was conducted. RESULTS: < 0.05). CONCLUSION: Although the treatment of dural arteriovenous fistulas by intravascular embolization has been widely used, the clinical effect of microsurgery is still better than that of endovascular embolization. Large scale and high-quality randomized controlled trials are required to validate the efficacy and safety of endovascular treatment in SDAVF patients.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".