MétaCan
Menu
Back to cohort
Record W1975496473 · doi:10.1081/clt-200041764

Delayed Dystonia Following Pimozide Overdose in a Child

2004· article· en· W1975496473 on OpenAlexaff
Robert D. Gair, Marjorie S. Friesen, Debra A. Kent, Allyson L. Davey

Bibliographic record

VenueJournal of Toxicology Clinical Toxicology · 2004
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsCentre for Drug Research and Development
Fundersnot available
KeywordsPimozideMedicineAnesthesiaDystoniaQT intervalIngestionDroolingBolus (digestion)HaloperidolSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Pimozide overdose has rarely been reported in children. In adults, pimozide intoxication may cause seizures, extrapyramidal and anticholinergic effects, hypotension, QTc prolongation and torsades de pointes. We report dystonia, hypotension and drowsiness following pimozide ingestion in a child. CASE REPORT: An alert 18-month-old presented to hospital 40 minutes after ingesting up to 6 mg (0.5 mg/kg) of pimozide. Vital signs: BP 91/62 mmHg, HR 130/min, RR 26/min, temperature 97.2 degrees F (36.2 degrees C). She received gastric lavage and activated charcoal. One hour later, her QTc interval was 420 msec, HR 150. She remained asymptomatic until 12 hours post-ingestion, when she developed drooling, tongue thrusting and drowsiness. BP was 75/40, HR 150, QTc 440 msec. BP increased to 95/50 after a bolus of normal saline. Her dystonia subsided over the next 12 hours without treatment. Drowsiness and tachycardia persisted until 40 hours post-ingestion. QTc interval at this time was 370 msec. Patient recovered without sequelae. CONCLUSION: Pimozide overdose in children may be associated with delayed onset of symptoms, including dystonia.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.398
Teacher spread0.362 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Toxicology Clinical ToxicologySame topicPoisoning and overdose treatmentsFrench-language works237,207