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Record W2164740567 · doi:10.1177/1078155213496675

Hazards in determining whether a drug is hazardous

2013· article· en· W2164740567 on OpenAlexaff
Nadine Badry, Joan Fabbro, Mário L de Lemos

Bibliographic record

VenueJournal of Oncology Pharmacy Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsHazardous wasteFormularyMedicineScope (computer science)Risk analysis (engineering)Occupational safety and healthEnvironmental healthPharmacologyEngineeringComputer science

Abstract

fetched live from OpenAlex

The US National Institute for Occupational Safety and Health list and evaluation criteria have provided an important foundation to help institutions identify and create a list of hazardous formulary drugs. However, further guiding principles were needed to make the adoption feasible at our organization. First, we developed separate directives for determining the inherent hazardous toxicity of a drug and for the requirements for safe handling based on dosage forms (exposure risks) of these drugs. Secondly, we created a systematic approach in determining the scope of the drugs reviewed by US National Institute for Occupational Safety and Health. Thirdly, we streamlined our review process by defining which drugs needed to be evaluated by our organization. Finally, we considered the pros and cons of creating a tiered system for classifying hazardous drugs beyond those recommended by US National Institute for Occupational Safety and Health.

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)0.070
metaresearch head score (Gemma)0.127
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: Commentary · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.127
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.005
Science and technology studies0.0020.005
Scholarly communication0.0040.006
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.073
GPT teacher head0.497
Teacher spread0.424 · 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
GenreCommentary

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

Citations2
Published2013
Admission routes1
Has abstractyes

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