Corpus callosal reference ranges: systematic review of methodology of biometric chart construction and measurements obtained
Bibliographic record
Abstract
OBJECTIVE: Adequate reference ranges of size of the corpus callosum (CC) are necessary to improve characterization of CC abnormalities and parental counseling. The objective of this study was to evaluate the methodology used in studies developing references charts for CC biometry. METHODS: We conducted a systematic review of studies on fetal CC biometry using a set of predefined quality criteria of study design, statistical analysis and reporting methods. We included observational studies whose primary aim was to create ultrasound or magnetic resonance imaging charts for CC size in a normal population of fetuses. Studies were scored against a predefined set of independently agreed methodological criteria, and an overall quality score was given for each study. RESULTS: Twelve studies met the inclusion criteria. Quality scores ranged between 17.4% and 95.7%. The greatest potential for bias was noted for the following items: sample selection and sample-size calculation, as only 17% of the studies were population-based and had consecutive or random recruitment of patients and with a justification of the sample size; number of measurements obtained for CC biometry, as only 17% of the studies performed more than one measurement per fetus and per scan; and description of characteristics of the study population, as only 8% of the studies clearly reported a minimum dataset of demographic characteristics. CONCLUSIONS: Our review demonstrates substantial heterogeneity in methods and final biometric values of the fetal CC across the evaluated studies. The use of uniform methodology of the highest quality is essential in order to define a 'short' CC and provide appropriate parental counseling. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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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 | 0.072 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".