P29The effectiveness of antenatal ultrasound in the detection of facial clefts
Bibliographic record
Abstract
Objective The aim of this study was to assess the sensitivity and specificity of antenatal ultrasound in the detection of facial clefts in a low risk screening population. Design This retrospective study covered a five year period from January 1993 to December 1997. From the study of antenatal, postnatal surgical and pathological records, a complete number of second trimestre fetuses with a cleft defect was identified. Correlation was made with the routine 18–20 week anomaly ultrasound examination in order to assess accuracy of diagnosis. Results After necessary exclusions there were 26 fetuses with cleft defects out of 23 577 live and stillbirths. The defect was detected in 17 of these 26 cases (65%). In 12 of these 17 (70.5%) the antenatal diagnosis was completely accurate, in the remaining 5 cases part of the defect was not detected. Conclusion In a low risk population it is possible to detect approximately two thirds of all fetuses with a cleft lip or palate. With the increasing emphasis on clinical governance, this study helps to set an achievable standard.
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 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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".