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Record W2379569595

Reviews of the Outpatient Prescriptions and Analysis of the Rational Drug Use in Kaifeng Maternity and Children Health Hospital

2015· article· en· W2379569595 on OpenAlexaboutno aff
Qin Mengchu

Bibliographic record

VenueZhongguo yiyuan yongyao pingjia yu fenxi · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionMedicineDrugOutpatient clinicQuarter (Canadian coin)Emergency medicinePediatricsFamily medicineInternal medicinePharmacology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the outpatient prescription in accordance with Management Methodso as to improve the level of rational use of drug. METHODS: A total of 1 800 outpatient prescriptions randomly collected in Kaifeng Maternity and Children Health Hospital in the fourth quarter during 2011-2013 were reviewed,evaluated and analyzed in accordance with Hospital Prescription Review Management Practices( Trial) .RESULTS: In the forth quarter of 2011,2012 and 2013,and average of 2. 42,2. 38 and 2. 32 drugs were prescribed per prescription,with antibiotic use prescribed in 45. 50%,35. 83% and 27. 33%,injections prescribed in 40. 17%,31. 17% and 26. 33%,essential drugs prescribed in 61. 58%,65. 59% and 72. 25%,generic drug name use in99. 70%,99. 75%,99. 86%, average drug cost per prescription of 86. 77 yuan,76. 35 yuan,64. 50 yuan,component ratio of 45. 71%,32. 62%,28. 63% in terms of outpatient drug consumption sum in the total,rate of rational prescriptions of 92. 17%,93. 33% and 95. 67%,respectively. CONCLUSION: Through review on the outpatient prescriptions,both the quality of prescriptions and the rational level of drug use have been improved significantly,which can help ensure medication safety.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.088
GPT teacher head0.350
Teacher spread0.262 · 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 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
Published2015
Admission routes1
Has abstractyes

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