Comparison of a computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians
Bibliographic record
Abstract
AIMS: To compare between computer analysis of intrapartum cardiotocography (CTG) features by the Omniview-SisPorto 3.5 and a consensus of clinicians. METHODS: Agreement study using 50 consecutively acquired tracings (206 h of signals) with >60 min duration, <10% signal loss and recorded in labor at term by internal fetal heart rate (FHR) monitoring. Tracings were divided into 10-min segments and independently analyzed by three experienced clinicians, in order to estimate the FHR baseline and identify periodic events. A consensus was reached using a three round Delphi procedure. Results were compared with the analysis provided by the Omniview-SisPorto 3.5 system. RESULTS: For baseline estimation, agreement between the computer and the consensus was high [intraclass correlation coefficient (ICC)=0.85; 95% confidence interval (CI) 0.46-0.93], with a mean difference of 3.7 bpm (limits of agreement -4.4-11.9 bpm), and 99% of differences under 15 bpm. A concordant identification was observed in 71% of accelerations (95% CI: 69%-73%), 68% of decelerations (95% CI: 66%-70%), and 87% of uterine contractions (95% CI: 85%-89%). CONCLUSIONS: A high agreement was observed between the Omniview-SisPorto 3.5 and a consensus of clinicians in evaluation of intrapartum CTG baseline, accelerations, decelerations and uterine contractions.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".