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Record W4401946506 · doi:10.1097/scs.0000000000010553

Timely Access for Orofacial Cleft Repairs in a Brazilian Amazon Center

2024· article· en· W4401946506 on OpenAlexaff
Franklin de Souza Rocha, S Salomão, Ayla Gerk, Ana Kim, Luiza Telles, Beatriz Laus Pereira Lima, Monica Melo de Carvalho, Cynthia Souza Martins Rocha, Nivaldo Alonso

Bibliographic record

VenueJournal of Craniofacial Surgery · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCraniofacialMedical recordRetrospective cohort studyPrimary carePediatricsDentistrySurgeryFamily medicine

Abstract

fetched live from OpenAlex

Orofacial clefts are the most common congenital craniofacial anomalies worldwide, and if not managed in a timely manner, can lead to significant complications. We aim to examine surgical timing at one of the few cleft care centers in the North region of Brazil since its foundation in 2016. This retrospective, descriptive study analyzed medical records from 2016 to 2023. We calculated the age at surgery for each time period and each primary surgery performed. We also evaluated the number of procedures performed outside the recommended age. Of the 1439 procedures performed from 2016 to 2023, 713 procedures met our inclusion criteria. Among these, 66.67% (n=188) of primary cheiloplasties, 67.80% (n=40) of primary lip adhesions, and 54.57% (n=203) of palatoplasties were performed outside the recommended time frame. Of the surgeries performed, 45.16% (n=322) were between 2016 and 2019, while 54.84% (n=391) were from 2020 to 2023. Considering procedures performed within the ideal recommended age groups, only 32.92% (n=106) were done between 2016 and 2019, in contrast to 45.01% (n=176) between 2020 and 2023. In conclusion, since the inception of the specialized center, there has been an increase in surgical volume and an improvement in their timing. However, many surgeries are still being conducted outside the recommended time frame.

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 score0.731
Threshold uncertainty score0.482

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 head0.030
GPT teacher head0.344
Teacher spread0.314 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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