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Record W2260384388 · doi:10.5863/1551-6776-13.2.65

Medication Errors: Neonates, Infants and Children Are the Most Vulnerable!

2008· article· en· W2260384388 on OpenAlexaff
Robert L. Poole, Bruce Carleton

Bibliographic record

VenueThe Journal of Pediatric Pharmacology and Therapeutics · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsChild and Family Research Institute
Fundersnot available
KeywordsMedicinePatient safetyPharmacyMedical emergencyComputerized physician order entryAdverse drug eventAdverse effectClinical pharmacyPediatricsHealth careIntensive care medicineFamily medicine

Abstract

fetched live from OpenAlex

Medical errors continue to plague the increasingly complex inpatient medical care system. Pediatric patient populations continueto be those most vulnerable to serious and sometimes fatal adverse drug events. Studies have shown that up to 93% of medication errors in children might have been prevented by computerized physician order entry (CPOE) and unit-based clinical pharmacists.1–4 A recent study reported that adverse drug event (ADE) rates in hospitalized children are substantially higher (15.7 per 1000 patient-days) than previously described.5 Professional organizations have provided detailed guidelines for preventing medication errors in pediatrics.6–8 With practice guidelines, prevention strategies and the benefits of unit based clinical pharmacists, why do these errors continue to happen? The following issues still need to be addressed by organizations treating pediatric patients, pharmaceutical manufacturers, medical software vendors and technology innovators:On April 11, 2008, The Joint Commission published a “Sentinel Event Alert” on preventing pediatric medication errors.10 This alert outlines pediatric specific risk reduction strategies for reducing medication errors: 1) Standardize and identify medications effectively, as well as the processes for drug administration. 2) Ensure full pharmacy oversight as well as the involvement of other appropriate staff-in the verifying, dispensing and administering of both neonatal and pediatric medications. 3) Use technology judiciously.Other Joint Commission suggested actions are also included in this document. Although most, if not all of these recommendations, are in place in the nation's Children's Hospitals, they are not common in hospitals where infants and children are only a small portion of the patient population. All hospitals that treat any infants or children should make every effort to make their medication—related systems and processes safe for the most vulnerable patients. Getting to zero errors will require the constant vigil of all healthcare professionals. CPOE, barcoding, the use of robotics and ADCs are tools that can help but they need careful application in the pediatric arena.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.386
Teacher spread0.331 · 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.

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

Citations18
Published2008
Admission routes1
Has abstractyes

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