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Record W2342923168

Counseling regarding pregnancy--related drug exposures by family physicians in Ontario.

2007· article· en· W2342923168 on OpenAlexaffabout
Jodi Goodwin, Scott Rieder, Michael Rieder, Doreen Matsui

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsWestern UniversityChildren's Hospital of Western Ontario
Fundersnot available
KeywordsMedicineFamily medicinePregnancyMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Family physicians may play a significant role in providing information to their patients on the effects of medication exposure during pregnancy. Women must receive accurate information, as unrealistic perception of teratogenic risk may lead to inadequate treatment of maternal disease or termination of otherwise wanted pregnancies. OBJECTIVES: To collect data on the current practices of family physicians in providing information regarding pregnancy-related drug exposures, in particular, their confidence in providing counseling and their sources of information. METHODS: A mailed survey was sent to a random sample of family physicians in Ontario. Outcome measures included the proportion of family physicians that feel confident in providing counseling regarding drugs in pregnancy, most common resources, barriers to counseling and preferences for future educational programs. RESULTS: Of the 756 surveys, 400 (53%) were returned, 265 (66%) by practicing physicians caring for women of childbearing age. Most (80.3%) felt confident in providing counseling, though a majority (56%) stated that available sources of information are not adequate. The most commonly consulted source was the Motherisk Program (62%). Lack of evidence-based information was cited as the major barrier. CONCLUSIONS: Although family physicians were confident in providing counseling to pregnant patients with regards to drug use, more than one-half thought that the available sources of information are not adequate. The dissemination of more evidence-based information in this field is needed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

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