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Record W4385759836 · doi:10.1093/ijpp/riad062

Recycling unused medications in hospitals is financially viable and good for the environment

2023· article· en· W4385759836 on OpenAlexaff
Isla Drummond, R. Bains, Anmol Dosanjh, Simroop Ladhar, Linda Tang, Deborah Heidary, Aaron M Tejani

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineIncinerationPharmacyWaste managementMedical emergencyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Considerable pharmaceutical waste is generated in hospital settings which can be reduced by recycling of unused medications. We sought to determine the recycling practices as well as quantify the volume and the value of oral solid medications returned from nursing units to the pharmacy departments at three urban hospitals. METHODS: Unused oral solid medications were recycled at three sites and the net financial impact of this practice was calculated (cost recovered - labour costs). The results were extrapolated to all 21 hospitals within the health system. KEY FINDINGS: Recycling medications in 21 hospitals could divert ~461 000 units of medication from the incinerator, with an estimated net value of ~$415 000 per year. CONCLUSIONS: Recycling unused medications could save substantial amounts of money and reduce negative environmental impacts from disposal/incineration.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.042
GPT teacher head0.407
Teacher spread0.365 · 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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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