Meta-analysis of surgical treatment and non-operative treatment for traumatic subdural effusion in adults
Bibliographic record
Abstract
Objective To compare the efficacy and prognosis between surgical treatment and non-operative treatment for traumatic subdural effusion (TSE) adult in adults. Methods Wanfang data, VIP database, CNKI, PubMed, EMBase, Cochrane were searched for the relevant papers published between 1985 and 2015. According to the inclusion and exclusion criteria, quality of the papers enrolled was evaluated using the Jadad scale and Newcastle-Ottawa Scale (NOS). RevMan 5.2 software was used to have a meta-analysis on the extracted data between operation group and non-operation group in recent cure rate, short-term effective rate, recent inefficiency, recent chronic subdural hematoma (CSDH) incidence, early mortality, long-term cure rate and long-term CSDH incidence, and bias analysis of the results was conducted. Results Nine papers with a total of 824 patients were enrolled in this meta-analysis. There were no significant differences between the two groups with respect to recent cure rate (OR=2.39, 95%CI 0.72~7.88, P>0.05), recent inefficiency(OR=0.85, 95%CI 0.12-6.01, P>0.05), recent CSDH incidence (OR=0.79, 95%CI 0.35-1.76, P>0.05) and early mortality (OR=2.88, 95%CI 0.13-61.55, P>0.05). Between-group differences were significant in short-term effective rate (OR=0.20, 95%CI 0.06-0.64, P<0.01), long-term cure rate (OR=0.32, 95%CI 0.14-0.71, P<0.01), long-term CSDH incidence(OR=3.14, 95%CI 1.42-6.97, P<0.01). Bias analysis showed no publication bias in the long-term outcome of the enrolled papers. Conclusion For TSE patients, surgical treatment exhibits significantly better results in recent effective rate, long-term cure rate and long-term CSDH incidence than non-operative treatment. Key words: Subdural effusion, traumatic; Brain injuries; Meta-analysis
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.055 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".