The operation methods and curative effect of periprosthetic fracture after hip replacement
Bibliographic record
Abstract
Objective To investigate the effect of different operation modes selection for periprosthesis fractures after hip replacement.Methods Totally 56 cases of periprosthetic fractures after hip replacement were selected and divided into three groups according to the Vancouver classification criteria and the efficacy after operation was observed.Results VAS scores of the three groups after treatment were lower than those before therapy(t =6.630,10.756,5.738,P =0.000) while Harris scores of the three groups after treatment were higher than those before treatment(t = 36.290,57.814,33.164,P = 0.000);differences among scores of the three groups had statistical significance in VAS score and Harris score(F = 13.581,P = 0.012;F = 8.632,P = 0.006),type A was better than that of the type B and typeC(P 0.05);the operation time,bleeding volume during operation and the healing time of the three groups had statistical significance(F = 13.186,P = 0.011;F = 8.0722,P = 0.0094;F = 9.835,P = 0.009);type A needed less time than type B and typeC(P 0.05 or 0.01);the adverse reaction rates had no significant difference(χ2= 1.532,P = 0.465).Conclusion Selecting the operation mode according to the specificity of periprosthetic fracture type and the patients can obtain the best treatment outcome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".