Bibliographic record
Abstract
Background: Perioperative stroke is a rare but serious complication of spinal surgery. However, it has been reported that there are multiple risk factors that contribute to postoperative stroke, but still remains controversial. The aim of this study is to investigate the risk factors of stroke after spinal surgery. Methods: A systematic search of relevant articles is published in PubMed, Embase, Web of Science, Cochrane Library and Clinical Trials databases until August 2022. According to the inclusion and exclusion criteria, two reviewers independently performed literature screening, data extraction and quality assessment of the obtained literature. The Newcastle-Ottawa Scale (NOS) score was used for quality assessment, and STATA 16.0 software was used for meta-analysis. Results: A total of 1706 relevant articles were initially identified and 13 articles were finally included in this study for data extraction and meta-analysis. The meta-analysis showed that advanced age, hypertension and diabetes mellitus were the risk factors for stroke after spinal operation. The OR values (95%CI) of these three factors were 3.36 (1.81, 6.24), 1.61 (1.26, 2.06) and 2.07 (1.23, 3.49) respectively. Conclusions: Advanced age, hypertension and diabetes mellitus are the current risk factors for postoperative cerebrovascular accidents (CVA).
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.063 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".