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Record W4415728341 · doi:10.20529/ijme.2025.082

DEG deaths: Why is India unable to stop them?

2025· article· en· W4415728341 on OpenAlexaff

Bibliographic record

VenueIndian Journal of Medical Ethics · 2025
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsScrutinyLegal actionTragedy (event)Action (physics)DrugDrug industry

Abstract

fetched live from OpenAlex

The deaths of children in Chhindwara in Madhya Pradesh from cough syrup adulterated with diethylene glycol (DEG) have laid bare the gaps in drug regulation, from the manufacturing site down to the pharmacist. They have exposed, as hollow, the governments' and drug regulators' announcements of higher manufacturing standards for medicines, regular inspections of manufacturing units and stringent action against those found flouting regulations - after similar mass poisonings from DEG in cough syrup reported in 2020, 2022 and 2023. The Chhindwara tragedy has revealed a drug regulatory system that has not only failed repeatedly to make medicines safe, but also actively fights any public scrutiny of its functioning.

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)0.006
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.002

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 head0.046
GPT teacher head0.384
Teacher spread0.337 · 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
Published2025
Admission routes1
Has abstractyes

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