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Record W3186936651 · doi:10.3390/ijerph18147526

Diagnosis of Fetal Alcohol Spectrum Disorders (FASDs): Guidelines of Interdisciplinary Group of Polish Professionals

2021· article· en· W3186936651 on OpenAlexaboutno aff
Katarzyna Okulicz‐Kozaryn, Agnieszka Maryniak, Magdalena Borkowska, Robert Śmigiel, Katarzyna Anna Dyląg

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationContext (archaeology)Fetal alcohol syndromeMedicineFetal Alcohol Spectrum DisorderFetal alcoholPrenatal alcohol exposureGuidelineBest practiceFamily medicinePediatricsMedical educationAlcoholPregnancyPathology

Abstract

fetched live from OpenAlex

(1) Background: Considerable prevalence in Poland and serious health consequences of prenatal alcohol exposure indicated the need to develop national guidelines for the diagnosis of fetal alcohol spectrum disorders (FASDs). It was assumed that the guidelines must be in line with international standards but adjusted to the Polish context. (2) Methods: Work on recommendations was carried out by an interdisciplinary team of Polish specialists. Its first stage was to assess the usefulness in our country of the U.S. and Canadian guidelines. In the second stage, after several rounds of discussions, a consensus was achieved. (3) Results: The Polish guidelines for diagnosing FASD cover the following issues: 1. distinguished diagnostic categories; 2. diagnostic procedure; 3. assessment of prenatal exposure to alcohol; 4. assessment of sentinel facial dysmorphias; 5. assessment of body weight, height, and head circumference; 6. neurodevelopmental assessment. An important element of the recommendation is appendices containing practical tools that are useful in the diagnostic procedure. (4) Conclusions: National guidelines may improve the quality and standardization of FASD diagnosis in Poland, but their practical utility has to be monitored.

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)0.002
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 score0.210
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.073
GPT teacher head0.438
Teacher spread0.365 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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