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Record W2068929433 · doi:10.4212/cjhp.v65i3.1141

Presence and Accuracy of Drug Dosage Recommendations for Continuous Renal Replacement Therapy in Tertiary Drug Information References

2012· article· en· W2068929433 on OpenAlexafffundvenue
Sean K Gorman, Richard S Slavik, Stefanie Lam

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsJewish General HospitalInterior HealthNova Scotia Health AuthorityCapital District Health Authority
FundersDalhousie University
KeywordsMedicineDoseConcordanceDrugFormularyIntensive care medicineDatabaseRenal replacement therapyDosage formPharmacologyInternal medicine

Abstract

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Background: Clinicians commonly rely on tertiary drug information references to guide drug dosages for patients who are receiving continuous renal replacement therapy (CRRT). It is unknown whether the dosage recommendations in these frequently used references reflect the most current evidence.Objective: To determine the presence and accuracy of drug dosage recommendations for patients undergoing CRRT in 4 drug information references.Methods: Medications commonly prescribed during CRRT were identified from an institutional medication inventory database, and evidence-based dosage recommendations for this setting were developed from the primary and secondary literature. The American Hospital Formulary System—Drug Information (AHFS–DI), Micromedex 2.0 (specifically the DRUGDEX and Martindale databases), and the 5th edition of Drug Prescribing in Renal Failure (DPRF5) were assessed for the presence of drug dosage recommendations in the CRRT setting. The dosage recommendations in these tertiary references were compared with the recommendations derived from the primary and secondary literature to determine concordance.Results: Evidence-based drug dosage recommendations were developed for 33 medications administered in patients undergoing CRRT. The AHFS–DI provided no dosage recommendations specific to CRRT, whereas the DPRF5 provided recommendations for 27 (82%) of the medications and the Micromedex 2.0 application for 20 (61%) (13 [39%] in the DRUGDEX database and 16 [48%] in the Martindale database, with 9 medications covered by both). The dosage recommendations were in concordance with evidence-based recommendations for 12 (92%) of the 13 medications in the DRUGDEX database, 26 (96%) of the 27 in the DPRF5, and all 16 (100%) of those in the Martindale database. Conclusions: One prominent tertiary drug information resource provided no drug dosage recommendations for patients undergoing CRRT. However, 2 of the databases in an Internet-based medical information application and the latest edition of a renal specialty drug information resource provided recommendations for a majority of the medications investigated. Most dosage recommendations were similar to those derived from the primary and secondary literature. The most recent edition of the DPRF is the preferred source of information when prescribing dosage regimens for patients receiving CRRT.RÉSUMÉContexte : Les cliniciens s’appuient couramment sur des sources tertiaires d’information pour guider les posologies médicamenteuses chez les patients sous traitement continu de remplacement de la fonction rénale (TCRFR). On ignore si les recommandations posologiques dans ces sources couramment utilisées reflètent les données probantes les plus actuelles.Objectif : Déterminer la présence et l’exactitude des recommandations sur la posologie des médicaments utilisés chez les patients sous TCRFR dans quatre sources d’information sur les médicaments.Méthodes : On a dressé la liste des médicaments couramment prescrits durant le TCRFR à partir d’une base de données des médicaments en inventaire dans un établissement, puis on a défini des recommandations posologiques fondées sur des données probantes issues de la literature primaire et secondaire. L’American Hospital Formulary System—Drug Information (AHFS–DI), le Micromedex 2.0 (en particulier les bases de données DRUGDEX et Martindale) et la 5e édition de Drug Prescribing in Renal Failure (DPRF5) ont été évalués à la recherche de recommandations posologiques sur des médicaments utilisés en cours de TCRFR. Les recommandations posologiques dans ces sources tertiaires ont été comparées aux recommandations tirées de la littérature primaire et secondaire pour établir une concordance.Résultats : Des recommandations posologiques fondées sur des données probantes ont été rédigées pour 33 médicaments administrés aux patients sous TCRFR. L’AHFS–DI n’a fourni aucune recommendation posologique spécifique au contexte du TCRFR, alors que la DPRF5 a fourni des recommandations posologiques pour 27 (82%) des médicaments et le logiciel d’application Micromedex 2.0 pour 20 (61%) des médicaments (13 [39%] dans la base de données DRUGDEX et 16 [48%] dans la base de données Martindale, dont 9 médicaments traits dans les deux sources). Les recommandations posologiques concordaient avec celles fondées sur des données probantes pour 12 (92%) des 13 médicaments de la base de données DRUGDEX, 26 (96%) des 27 médicaments de la DPRF5 et tous les 16 médicaments (100%) de la base de données Martindale.Conclusions : Une importante source tertiaire d’information sur les médicaments n’a fourni aucune recommandation posologique sur des médicaments utilisés chez les patients sous TCRFR. En revanche, deux des bases de données comprises dans une application Web d’information médicale et la dernière édition d’une source d’information sur les médicaments utilisés en néphrologie ont fourni des recommandations pour la majorité des médicaments examinés. La plupart des recommandations posologiques étaient similaires à celles tirées des sources d’information primaires et secondaires. La plus récente édition du DPRF est la source préférée pour l’établissement des schemas posologiques chez les patients qui reçoivent un TCRFR.

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)0.026
metaresearch head score (Gemma)0.255
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 score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.255
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0200.015
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.406
Teacher spread0.355 · 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".

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Citations3
Published2012
Admission routes3
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