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Record W1970007898 · doi:10.1155/2009/585269

The Effect of Acid-Reducing Pharmacotherapy on the Severity of Nausea and Vomiting of Pregnancy

2009· article· en· W1970007898 on OpenAlexafffund
Simerpal K. Gill, Caroline Maltepe, Katayoon Mastali, Gideon Koren

Bibliographic record

VenueObstetrics and Gynecology International · 2009
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenDuchesnay
KeywordsAlgorithmVomitingNauseaMedicineMaterials scienceComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Background. Heartburn and acid reflux (HB/RF) are associated with increased severity of nausea and vomiting. The ability of acid-reducing drugs to reduce symptoms of nausea and vomiting of pregnancy has not been previously tested. Objective. To determine whether acid-reducing pharmacotherapy decreases the severity of NVP symptoms. Methods. We studied a cohort of women experiencing NVP, who were also experiencing HB/RF. Women were counseled to commence acid-reducing pharmacotherapy. The effectiveness of the acid-reducing medication in decreasing symptoms of both HB/RF and NVP was measured. Results. Acid-reducing drugs resulted in significant decreases in PUQE (9.6 +/- 3.0 to 6.5 +/- 2.5, P < .0001) and well-being scores from the initial (4.0 +/- 2.0) to the follow-up interview (6.8 +/- 1.6, P < .0001). After intervention with acid-reducing pharmacotherapy, a reduction in acid symptoms correlated significantly with reduction in NVP (R(2) = 0.72, P < .001). Conclusion. This is the first study to demonstrate that management of HB/RF can reduce the severity of NVP.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.293
Teacher spread0.283 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations91
Published2009
Admission routes2
Has abstractyes

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