Hip surveillance for children with cerebral palsy: Are the Australian standards the gold standard?
Bibliographic record
Abstract
Although surveillance for hip displacement in children with cerebral palsy (CP) was routine practice in Australia since 1997, these surveillance programs varied widely between states. This prompted efforts to convene a national working group to standardize a hip surveillance program for this population, culminating with the publication of the “Consensus Statement on Hip Surveillance for Children with Cerebral Palsy: Australian Standards of Care” [1]. These recommendations, comprehensively detailed in this edition of the journal, have been formally adopted since 2008. They aim to reduce the morbidity associated with hip displacement in CP by facilitating early detection and timely referral, which will presumably lead to early and more efficient or cost-effective management of hip displacement. (Child health) surveillance is the systematic and ongoing collection, analysis, and interpretation of indices of growth and development, which includes gathering data from screening tests (in this case clinical and radiologic tests) in order to identify, investigate, and, where appropriate, correct deviations from predetermined norms [2]. Hip surveillance, in essence, serves as a screening program aimed at identifying children who could potentially benefit from early intervention: in this case, a form of tertiary prevention to impede the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".