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Record W2348850608

[Amifostin in protection of kidney from cisplatinum injury].

2002· article· en· W2348850608 on OpenAlexaff
Huijuan Cui, Shujun Zhang, Peiwen Li, Zhongzhen Guan, Xiaofei Sun, Keng Shen, Ming Wu, Xiaodian Hu, Shujun Liu, Lijun Di, Shucai Zhang

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsBlood urea nitrogenMedicineNauseaInternal medicineRenal functionVomitingGastroenterologyLung cancerGroup BKidneyGroup AUrology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate Amifostin's effect on protecting kidney from cisplatinum (DDP) injury and its adverse reactions and safety. METHODS: 193 Patients were divided into two groups randomly: 102 in group A (treatment group) and 91 in group B (control group). Indexes such as blood routine, blood calcium, liver function, blood urea nitrogen (BUN), cretinine (C), and urinary N-acetyl-beta-D-glucosaminidase (NAG)/C and micro-albumin (MAB/C) were monitored at different intervals before or after treatment. RESULTS: In the two courses of treatment in both groups, the deviation (D) values of MAB/C before treatment and on D2 in group A were lower than those in grop B (P < 0.05), so were those before treatment and on D4, D6, D10 and D14 (P < 0.01). The D-values of NAG/C before treatment and on D4, D6, D10 and D14 in the first course of group A were obviously lower than those on the corresponding days in group B (P < 0.01), so were those before treatment and on D2, D4, D6, D10 and D14 in the second course (P < 0.01). CONCLUSION: The reduction of MAB/C and NAG/C by Amifostin in group A demonstrates that: Amifostin is able to effectively protect the renal function, regardless of the type of tumor. In contrast with group B, Amifostin in group A shows no protection for tumor in lung cancer and ovarian cancer. The main side effects of Amifostin are mild hypotension, nausea, vomiting and hypocalcemia in some patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.219
Teacher spread0.183 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2002
Admission routes1
Has abstractyes

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