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Record W2137737058 · doi:10.4103/0970-9185.117105

Accidental intravenous infusion of a large dose of magnesium sulphate during labor: A case report

2013· article· en· W2137737058 on OpenAlexaff
Kamal Kumar, Arif Al Arebi, Indu Singh

Bibliographic record

VenueJournal of Anaesthesiology Clinical Pharmacology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsVictoria HospitalWestern University
Fundersnot available
KeywordsMedicineAccidentalConfusionHarmAnesthesiaMagnesiumLoading doseIntensive care medicineMedical emergencyEmergency medicine

Abstract

fetched live from OpenAlex

During labor and child delivery, a wide range of drugs are administered. Most of these medications are high-alert medications, which can cause significant harm to the patient due to its inadvertent use. Errors could be caused due to unfamiliarity with safe dosage ranges, confusion between similar looking drugs, mislabeling of drugs, equipment misuse, or malfunction and communication errors. We report a case of inadvertent infusion of a large dose of magnesium sulphate in a pregnant woman.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.002
Science and technology studies0.0050.004
Scholarly communication0.0030.005
Open science0.0030.004
Research integrity0.0130.007
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 head0.057
GPT teacher head0.473
Teacher spread0.416 · 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 designCase report
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

Citations12
Published2013
Admission routes1
Has abstractyes

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