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Record W2512461477 · doi:10.1002/ajmg.c.31522

Medical interventions and survival by gender of children with trisomy 18

2016· article· en· W2512461477 on OpenAlexaff
Jennifer H. Donovan, Genomary Krigbaum, Deborah A. Bruns

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPsychological interventionMedicineTrisomyIncidence (geometry)PediatricsIntervention (counseling)Enteral administrationSurgeryPsychiatryParenteral nutrition

Abstract

fetched live from OpenAlex

Research has typically shown limited aggressive medical interventions and low survival rates for children with full trisomy 18. Recent studies provide more positive results. This study examined 82 children with full trisomy 18 drawn from the Tracking Rare Incidence Syndromes (TRIS) project database. Children were classified into three groups according to the highest intervention received: "hospice or no intervention" (n = 5, 6.1%), "necessary interventions (enteral feeding, ventilator use)" (n = 46, 56.1%), and "aggressive interventions (surgery)" (n = 31, 37.8%). Seven of 14 male children (50%) and 52 of 68 female children (76.5%) were living at the time of survey completion. Additionally, information about any interventions used during the care of these children was also provided. It was found that three males (37.5%) and 28 females (48.3%) had used hospice care at some point; 12 males (85.7%) and 61 females (89.7%) received enteral feeding at some point; 7 males (58.3%) and 25 females (38.5%) had ventilator; and 7 males (50%) and 33 females (48.5%) underwent some form of surgery. These results suggest improved outcomes when given necessary and aggressive medical interventions. Implications and recommendations for further research are provided. © 2016 Wiley Periodicals, Inc.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.433
Teacher spread0.377 · 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.

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

Citations15
Published2016
Admission routes1
Has abstractyes

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