Centralization of cleft care in the UK. Part 6: a tale of two studies
Bibliographic record
Abstract
OBJECTIVES: We summarize and critique the methodology and outcomes from a substantial study which has investigated the impact of reconfigured cleft care in the United Kingdom (UK) 15 years after the UK government started to implement the centralization of cleft care in response to an earlier survey in 1998, the Clinical Standards Advisory Group (CSAG). SETTING AND SAMPLE POPULATION: A UK multicentre cross-sectional study of 5-year-olds born with non-syndromic unilateral cleft lip and palate. Data were collected from children born in the UK with a unilateral cleft lip and palate between 1 April 2005 and 31 March 2007. MATERIALS AND METHODS: We discuss and contextualize the outcomes from speech recordings, hearing, photographs, models, oral health and psychosocial factors in the current study. We refer to the earlier survey and other relevant studies. RESULTS: We present arguments for centralization of cleft care in healthcare systems, and we evidence this with improvements seen over a period of 15 years in the UK. We also make recommendations on how future audit and research may configure. CONCLUSIONS: Outcomes for children with a unilateral cleft lip and palate have improved after the introduction of a centralized multidisciplinary service, and other countries may benefit from this model. Predictors of early outcomes are still needed, and repeated cross-sectional studies, larger longitudinal studies and adequately powered trials are required to create a research-led evidence-based (centralized) service.
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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 | 0.064 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".