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Infection Control in the Laboratory

2023· other· en· W4406352253 on OpenAlexaff
Michael Pentella, Paul M. Luethy, Deirdre L. Church

Bibliographic record

VenueClinMicroNow · 2023
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiosafetyVaccinationImmunizationEnvironmental healthMedicineOccupational exposurePublic healthOccupational diseaseContagious diseaseDiseaseImmunologyNursingPathology

Abstract

fetched live from OpenAlex

Abstract The immunization of laboratory staff is an important component of the laboratory biosafety plan and occupational health services. Immunization is the primary means of preventing the acquisition of disease in workers who have the potential for exposure to various microbial agents in the laboratory workplace. Personal health status must be considered by the individual in electing to be vaccinated or deferring on vaccination. This chapter describes immunization practices and procedures for specific functions in the clinical and public health laboratory setting. Employees who decline a recommended vaccination may be required to complete a vaccination declination form for retention in their occupational health record. Risk for occupational HIV infection is modified not just by the general type of exposure but also by several specific factors, including the depth of the exposure, the exposure inoculum, and the exposed worker's immunological response.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.008

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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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