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Record W4308434460 · doi:10.12968/jprp.2022.4.11.482

Non-medical midwifery prescribing of an antiemetic for a glucose tolerance test: a case study

2022· article· en· W4308434460 on OpenAlexaboutno aff
Natalie Anderson

Bibliographic record

VenueJournal of Prescribing Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
Fundersnot available
KeywordsAntiemeticMedicineTest (biology)Family medicineNursingAnesthesiaNausea

Abstract

fetched live from OpenAlex

This is a case study of a 26-week pregnant woman requiring an antiemetic before a glucose tolerance test and the prescribing decision that a non-medical midwifery prescriber needed to make in the outpatient setting. The case study demonstrates how the prescriber demonstrated the Royal Pharmaceutical Society competencies. The Calgary-Cambridge model was used to assess the patient. There are no local or national guidelines for this situation with very limited evidence available to assess the effectiveness of the different antiemetics. The consultant's advice in the local setting is to prescribe a cyclizine 50mg tablet. This case study evaluates the available evidence and guidelines and concludes that prescribing cyclizine in this scenario is reasonable in the absence of any good evidence.

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)0.012
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.370
Teacher spread0.323 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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