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Record W2029339196 · doi:10.1136/bmjquality.u134.w317

Improving the correct prescription and dosage of gentamicin

2012· article· en· W2029339196 on OpenAlexaff
Imran Qureshi, Mr. Hani Habayeb, Clive Grundy

Bibliographic record

VenueBMJ Quality Improvement Reports · 2012
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsReach Technologies (Canada)
Fundersnot available
KeywordsMedicineMedical prescriptionGentamicinPharmacologyIntensive care medicineAntibioticsPediatricsMicrobiologyBiology

Abstract

fetched live from OpenAlex

Gentamicin is an amino glycoside antibiotic, which is used predominantly in gram-negative infections but also has anti-staphylococcal activity. It is commonly used because of its lack of diffogenicity. There are recognised issues with its dosing of which time is a major factor. A baseline study demonstrated that dosing was only being carried out correctly 30% of the time. This has serious consequences for the treatment of infections. This quality improvement project intervened with the use of a gentamicin calculator, which enabled users to perform three complex mathematical equations simultaneously without error. Post measurement demonstrated that correct dosing had increased to 92%, which was a significant increase.

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)0.025
metaresearch head score (Gemma)0.149
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.149
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.043
GPT teacher head0.368
Teacher spread0.325 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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