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Record W4385212010 · doi:10.52711/2231-5691.2023.00020

A Study on Pharmaceutical Drug Recall

2023· article· en· W4385212010 on OpenAlex
Bansi l. Bhalodiya, Amitkumar J. Vyas, Ajay I. Patel, Ashvin Dudhrejiya, Ashok B. Patel

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Journal of Pharmaceutical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Quality and Counterfeiting
Canadian institutionsnot available
Fundersnot available
KeywordsRecallProduct (mathematics)MedicineDrugSchedulePharmacologyEnvironmental healthPsychologyEconomicsManagementMathematics

Abstract

fetched live from OpenAlex

The present study describes the pharmaceutical drug recall in different five countries to evaluate the drug recall that occurred in the last three successive years. The different countries have different regulations for drug recall. Drug product recall is an action taken to withdraw or remove a batch or an entire production run of drug product from distribution or use to return them to manufacturer.it is usually done due to deficiency in quality, safety and efficacy. In the USA, guidelines for drugs product recall are described under 21 CFR Parts 7, 107 and 1270. In Australia, guidelines for drugs product recall are described under section 65F of trade practices act 1974. In Canada, it includes under section 25 of Natural Health Products Regulations (NHPR). In India it includes under para 27 and 28 of schedule M. In South Africa SAHPRA (South African Health Products Regulatory Authority) guidelines are responsible for regulations of drug product recall. Majority of drug recalls occur in the United states due to various reasons. In 2020-2022 total 257 drugs were recalled in Last three years. In Canada and Australia 220 and 25 drugs are recalled respectively. India and South Africa have recalled 2 and 21 drugs respectively. By the observation we can conclude that India and South Africa have a smaller number of recalls. In the USFDA number of drug recalls are decreasing due to following up the laws and regulatory.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.002

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 head0.510
GPT teacher head0.612
Teacher spread0.102 · 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