Bibliographic record
Abstract
目的观察临床急救用药亚甲蓝对肌酐酶法(POD系统)测定的影响.方法采用肌酐酶法(POD系统)试剂在7020全自动生化分析仪上,模拟临床检测标本分别观察不同浓度亚甲蓝对样本空白、肌酐标准液和临床实测标本肌酐检测结果影响.结果亚甲蓝对肌酐酶法(POD系统)测定为负干扰:当亚甲蓝浓度为0.0100(mg/ml)时,样本空白结果开始为负值;当浓度850μmol/L的肌酐标准液中含亚甲蓝的浓度为0.005mg/ml时,其负干扰已经显现(结果下降为826.9μmol/L);对于临床实际检测标本,肌酐结果随着亚甲蓝的浓度增加,标本检测值逐渐下降,甚至达到负值.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".