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Early Detection of Allergy Symptoms in Children and Adolescents, Characteristics of Possible Pathogens, Pre-Treatment Prevention Measures (in Poland)

2024· article· en· W4396220050 on OpenAlexvenueno aff
Maria Zofia Lisiecka

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2024
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAllergyPediatricsEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

Background: The purpose of this study was to investigate the main allergens and signs of the onset of allergic diseases and explore methods of prevention that do not include medicines. Methods: Non-parametric statistical methods were used. A retrospective study was conducted, which included 270 case histories. The average age of the patients was 19 (12.5; 40.1) years, 127 (47%) were men and 143 (53%) were women. Clinical diagnoses, early symptoms, allergen spectrum, and treatment recommendations provided by doctors were analyzed. It was established that the early signs of allergic rhinitis and rhinoconjunctivitis are nasal congestion, runny nose, and lacrimation, and the onset of atopic dermatitis is -characterized by dry and itchy skin. Results: Allergy to triggers from one group was present in 136 (50.4%) patients, irritants from two groups – in 95 (35.2%) patients, and irritants from three groups – in 39 (14.4%) patients. Polish doctors advised patients to limit their exposure to allergens but did not give recommendations for concrete actions. Conclusion: The results of the study can be used to help in the identification of the most common symptoms of allergic diseases and allergens, which is vital for the early diagnosis of this pathology by clinicians.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueInternational Journal of Child Health and NutritionSame topicAllergic Rhinitis and SensitizationFrench-language works237,207