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Record W4388998508 · doi:10.34172/jaehr.1274

Medical Waste Management in Private Hospitals in Tehran

2023· article· en· W4388998508 on OpenAlex
Ali Hosseinzadeh, Mir Amir Mohammad Reshadi, Morteza Nazaripour, M. Katayoon Rezaei

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

VenueJournal of Advances in Environmental Health Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHospital wasteMedical wasteWaste managementChecklistMunicipal solid wasteMedicineBiomedical wasteSolid waste managementMedical emergencyHealth careEngineering

Abstract

fetched live from OpenAlex

Background: Solid waste management is one of the important aspects of the hospital management. Methods: In this study, we examined the quantity and composition of medical solid waste in eight private hospitals in Tehran. For this purpose, a checklist was used through interviews with hospital waste management staff as well as collecting information on hospital waste generation. The annual average of obtained data was analyzed in this study. Results: The results indicated that the private hospitals under study generated solid waste ranging from 24 to 1091 kg/day. The average medical waste generation in the studied privative hospitals was 4 kg/bed/day equal to 5.09 kg/patient/day. Common waste accounted for 70.73% of total hospital solid waste, while infectious and sharp waste accounted for 31.04% of the hospital solid waste. Infectious wastes were disinfected using autoclave in all hospitals. Conclusion: Segregation of infectious waste from hospital waste mass reduces the environmental and health risk of hospital waste and reduces the cost of waste management in private hospitals.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.637
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
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 head0.063
GPT teacher head0.451
Teacher spread0.388 · 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