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Record W2802989178 · doi:10.1128/cmr.00062-17

Practical Guidance for Clinical Microbiology Laboratories: Implementing a Quality Management System in the Medical Microbiology Laboratory

2018· review· en· W2802989178 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Microbiology Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuality management systemQuality assuranceMedical laboratoryOperationalizationEngineering managementCompetence (human resources)Good laboratory practiceQuality managementQuality (philosophy)Quality of analytical resultsProcess managementComputer scienceManagement systemEngineeringMedicineOperations managementExternal quality assessmentPsychologyPathology

Abstract

fetched live from OpenAlex

This document outlines a comprehensive practical approach to a laboratory quality management system (QMS) by describing how to operationalize the management and technical requirements described in the ISO 15189 international standard. It provides a crosswalk of the ISO requirements for quality and competence for medical laboratories to the 12 quality system essentials delineated by the Clinical and Laboratory Standards Institute. The quality principles are organized under three main categories: quality infrastructure, laboratory operations, and quality assurance and continual improvement. The roles and responsibilities to establish and sustain a QMS are outlined for microbiology laboratory staff, laboratory management personnel, and the institution's leadership. Examples and forms are included to assist in the real-world implementation of this system and to allow the adaptation of the system for each laboratory's unique environment. Errors and nonconforming events are acknowledged and embraced as an opportunity to improve the quality of the laboratory, a culture shift from blaming individuals. An effective QMS encourages "systems thinking" by providing a process to think globally of the effects of any type of change. Ultimately, a successful QMS is achieved when its principles are adopted as part of daily practice throughout the total testing process continuum.

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.

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)0.115
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1150.050
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.004
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0000.002

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 head0.358
GPT teacher head0.588
Teacher spread0.229 · 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