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Successful screening for neonatal hip instability in Australia

2002· article· en· W1970355259 on OpenAlexfundno aff
P W Goss

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2002
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
FundersMcMaster University
KeywordsMedicineIncidence (geometry)PediatricsPopulationPacific islanders

Abstract

fetched live from OpenAlex

OBJECTIVE: Australian screening programmes for congenital dislocation of the hip (CDH) are characterized by lower neonatal hip instability (NHI) detection rates than more successful international programmes. Through creating a quality, accountable clinical screening programme for NHI detection, the present study aimed to establish the true incidence of NHI in Australian babies and to eliminate 'late diagnosed' CDH. METHODS: Doctors responsible for routine neonatal care were made accountable for NHI detection and examined 5166 consecutive live births in the first days of life between 1989 and 2000. Techniques for clinical NHI detection were taught, and doctors practised with teaching-mannequins. Paediatricians clinically determined true positive NHI cases and managed them for a 12-month period. Peer review of NHI detection rates was introduced to encourage accountability. Surveillance for 'late diagnosed' CDH occurred regularly through a variety of methods. RESULTS: One hundred babies with NHI were detected (19.4 per 1000): 77% were female; 26% were breech presentation, 25% had a family history of hip instability; and all received some form of splinting. Follow up for 85% of these babies at 12 months revealed no significant complications. Extensive searching has revealed no baby with 'late diagnosed' CDH from the study population in 12 years. One baby commenced treatment late (at 4 months) because of a failure of process following early NHI detection. CONCLUSIONS: The true incidence of NHI in Australia is > or =19 per 1000 births. Successful clinical CDH screening programmes using primary care doctors can be created and might eliminate 'late diagnosed' CDH.

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 score0.044
Threshold uncertainty score0.273

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 head0.044
GPT teacher head0.330
Teacher spread0.285 · 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

Citations19
Published2002
Admission routes1
Has abstractyes

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