Three-dimensional camera anthropometry to assess risk of cephalopelvic disproportion-related obstructed labour in Ethiopia
Bibliographic record
Abstract
Cephalopelvic disproportion (CPD)-related obstructed labour requires delivery via Caesarean section (C/S); however, in low-resource settings around the world, facilities with C/S capabilities are often far away. This paper reports three low-cost tools to assess the risk of CPD, well before labour, to provide adequate time for referral and planning for delivery. We performed tape measurement- and three-dimensional (3D) camera-based anthropometry, using two 3D cameras (Kinect and Structure) on primigravida, gestational age ≥ 36 weeks, from Addis Ababa, Ethiopia. Novel risk scores were developed and tested to identify models with the highest predicted area under the receiver-operator characteristic curve (AUC), detection rate (true positive rate at a 5% false-positive rate, FPR) and triage rate (true negative rate at a 0% false-negative rate). For tape measure, Kinect and Structure, the detection rates were 53%, 61% and 64% (at 5% FPR), the triage rates were 30%, 56% and 63%, and the AUCs were 0.871, 0.908 and 0.918, respectively. Detection rates were 77%, 80% and 84% at the maximum J -statistic, which corresponded to FPRs of 10%, 15% and 11%, respectively, for tape measure, Kinect and Structure. Thus, tape measurement anthropometry was a very good predictor and Kinect and Structure anthropometry were excellent predictors of CPD risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".