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Record W4297093854 · doi:10.7759/cureus.29623

An Assessment of Risk Factors of Delayed Speech and Language in Children: A Cross-Sectional Study

2022· article· en· W4297093854 on OpenAlexaff
Anish Kumar, Maryam Zubair, Azouba Gulraiz, Sruti Kalla, Saif Khan, Srushti Patel, Maria F Fleming, Princess T Oghomitse-Omene, Parth Patel, Muhammad Saqlain S Qavi

Bibliographic record

VenueCureus · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCovenant Health
Fundersnot available
KeywordsMedicineLanguage developmentSpoken languageSpeech disorderEtiologyCued speechAudiologyDevelopmental psychologyLinguisticsPsychologyPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

Introduction Communication is the exchange of information through speaking, writing, and other mediums. Speech is the expression of thoughts in spoken words. Language is the principal method that humans use for relaying information; consisting of words conveyed by speech, writing, or gestures. Language is the conceptual processing of communication. Problems in communication or oral motor function are called speech and language disorders. Developmental delay is diagnosed when a child does not attain normal developmental milestones at the expected age. Speech and/or language disorders are amongst the most common developmental difficulties in childhood. Such difficulties are termed 'primary' if they have no known etiology, and 'secondary' if they are caused by another condition such as hearing and neurological impairment, and developmental, behavioral, or emotional difficulties. Objectives The objective of our study was to observe the risk factors for speech and language delay in the children presenting to the speech therapy clinic of a tertiary care hospital in a large urban center. Methodology A cross-sectional study was conducted on 150 children presenting at the speech therapy clinic of Lahore General Hospital from July to August 2021. A well-designed questionnaire was used to collect data about the sociodemographic profile, and biological, developmental, and environmental risk factors of speech and language delay in children. SPSS, version 25 (IBM Corp., Armonk, NY) was used to enter and analyze the data. Results Parents or caretakers of a total of 98 male and 52 female children took part in this study aged 2-11 years. The average age of speech and language delay among the children was 5.65 years, 66.7% of which went to normal school while 31.3% went to special school; 66.7% were from urban areas. Around 60% had middle ear infections, and 34.7% were found to have oropharyngeal anomalies. A history of intrapartum complications was found in 68.4% of children; 46.7% of children had a history of use of a pacifier and 38% had a history of thumb sucking. Nearly 39% of children belonged to a multilingual family environment and 66.7% had a family history of screen viewing for more than two hours. Conclusion The major risk factors contributing to speech and language delay in children are family history of speech and language delay, prolonged sucking habits, male gender, oropharyngeal anomalies, hearing problems, and middle ear infections. Measures should be taken to educate people regarding risk factors, courses, and management of speech and language delay in children.

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)0.002
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.371
Teacher spread0.356 · 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".

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Citations23
Published2022
Admission routes1
Has abstractyes

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