Bibliographic record
Abstract
目的 探讨全髋关节置换(THA)术中、术后股骨假体周围骨折(PFF)的治疗方法及疗效.方法 回顾性分析2002年1月至2009年12月收治的24例THA术中、术后PFF患者资料,男13例,女11例；年龄33 ～ 85岁,平均63.6岁.骨折根据Vancouver分型:A型7例,B型12例(B1型3例,B2型8例,B3型1例),C型5例.术中出现PFF 5例,术后外伤所致PFF 19例.非手术治疗4例,手术治疗20例.手术方式包括钢丝环扎、记忆合金环抱器、锁定钢板、长柄翻修假体结合植骨等.结果 所有患者术后获8 ～52个月(平均23.8个月)随访.24例患者骨折获骨性愈合,愈合时间为3～11个月,平均5.5个月.末次随访时髋关节Harris评分平均为80.3分(68 ～95分),其中优7例,良12例,可4例,差1例,优良率为79.2％.1例B2型骨折患者保守治疗后发生畸形愈合. 结论 对于THA术中、术后PFF,治疗方案需结合骨折部位、假体有无松动、局部骨质量及身体状况而定,其治疗原则是移位骨折需进行牢固固定,松动假体要进行翻修,严重骨缺损需要植骨处理。
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".