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Record W2531563104 · doi:10.1597/16-095

Time Trends and Determinants of Fistula in Cleft Patients at BC Children's Hospital, Canada

2016· article· en· W2531563104 on OpenAlexaboutno aff
Negar Salimi, Jolanta Aleksejüunienė, Edwin H. Yen, Angelina Y.C. Loo

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFistulaPediatricsGeneral surgeryFamily medicineDemographySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the time trends and determinants of palatal fistula in children with different types of cleft at British Columbia's Children's Hospital between 1995 and 2012. METHODS: A total of 558 medical charts of nonsyndromic patients with cleft lip and palate were eligible for the chart review. The occurrence of primary palatal fistula was assessed at any time throughout the patient's total observation period. Three types of clefts were recorded: unilateral cleft lip and palate (ULCLP), bilateral cleft lip and palate (BLCLP), and isolated cleft palate (ICP). Cleft severity, time period of treatment, type of surgery and surgeon's experience were tested as determinants. RESULTS: Of all 558 patients, 228 had ULCLP, 226 had ICP, and 104 had BLCLP. The combined postoperative palatal fistula rate was 28%. The significant differences in fistula rates related to type of cleft (patients with BLCLP had the highest fistula rates), time period (rates were higher in earlier years than in later years), type of surgery (highest rates were for two-flap palatoplasty), and surgeons with less experience. CONCLUSIONS: Almost one quarter of the patients, developed fistula, and fistula incidence declined after 2009. The higher fistula rates were determined by cleft severity, time period of treatment, type of surgery, and surgeon's experience.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

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