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Efficienza di un kit regionale indigeno amazzonico nello screening uditivo neonatale a Belém do Pará

2024· article· it· W4393853470 on OpenAlexaff
Roberta Ferraz Almeida, Amanda Alves Fecury, Carla Viana Dendasck, Cláudio Alberto Gellis de Mattos Dias

Bibliographic record

VenueRevista Científica Multidisciplinar Núcleo do Conhecimento · 2024
Typearticle
Languageit
FieldHealth Professions
TopicIndigenous Health and Education
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

La perdita dell’udito può verificarsi a diversi livelli fisiologici e gradi, colpendo principalmente i bambini. La Legge n. 12.303/2010, nota come Test dell’Orecchietta, obbliga alla realizzazione di uno screening uditivo nei neonati. Diversi strumenti sonori non calibrati possono essere utilizzati per effettuare uno screening uditivo comportamentale: flauto di plastica, battito di cucchiaio su una tazza, giocattoli di gomma, tamburi, maracas di paglia e maracas di zucca, con gli ultimi tre di origine indigena. Lo scopo di questo studio è verificare l’efficacia di un kit regionale indigeno amazzonico nello screening uditivo comportamentale neonatale a Belém do Pará, per questo è stato condotto uno studio quantitativo descrittivo. Il kit uditivo valutato con strumenti regionali si è dimostrato efficace, valutando i principali riflessi del neonato. Ha inoltre dimostrato la semplicità di un metodo di rilevazione e la fattibilità della sua inclusione nei metodi di screening di routine nel servizio neonatale, consentendo la diagnosi e il follow-up precoce della sordità nei neonati, il che può rappresentare un’alternativa, soprattutto in luoghi che non dispongono delle apparecchiature tradizionali.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0090.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.009

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 head0.046
GPT teacher head0.377
Teacher spread0.331 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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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