Meta-analysis of application of Ottawa knee rules in knee injuries
Bibliographic record
Abstract
目的 采用循证医学的Meta分析方法研究膝关节骨折快速诊断规则(OKR),明确OKR诊断膝关节骨折的准确性,探讨膝关节外伤X线摄片合理选择的必要性.方法 收集1990年1月～2006年5月国内外公开发表的OKR诊断膝关节骨折的文献,按Cochrane诊断组建议的对Meta分析的质量要求对符合条件的原始文献进行质量评估,提取有效文献的数据进行合并分析,计算OKR的敏感性和特异性,阳性似然比和阴性似然比,并做诊断试验的SROC曲线.同时对文献的异质性和偏倚进行评价.结果 共检索到文献131篇,符合纳入标准的共6篇,均为成人OKR对膝关节骨折的准确性研究,合并敏感性为100%,合并特异性为49%,阳性似然比为1.91,阴性似然比为0.024.结论 OKR在膝关节骨折的诊断方面具有较高的临床应用价值,可以减少30%～40%的不必要X线摄片,节约医疗资源,降低医疗费用,减少患者在急诊处理的等待时间.
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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 | 0.059 | 0.115 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.076 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".