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Record W32670610 · doi:10.1186/s40900-020-00206-5

亚甲蓝对肌酐酶法(POD系统)测定的影响

2004· article· en· W32670610 on OpenAlexfundno aff
李安久, 张绪红, 张秀香, 季雪兰

Bibliographic record

Venue江西医学检验 · 2004
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.395
GPT teacher head0.507
Teacher spread0.111 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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