Computerized Labor Management – a new approach to labormanagement
Bibliographic record
Abstract
OBJECTIVE: To accurately and continuously measure cervical dilatation and fetal head station in labor. STUDY DESIGN: A low cost ultrasound-based computerized labor management system was designed; The efficacy and safety of the attachment device was the subject of the first clinical study(n=20), while the second (n=82)focused on the performance of the system. This pilot clinical measurements of the cervical dilatation and fetal head station were verified against standard digital examination. RESULTS: The laboratory testings resulted in accurate distance measurements with error of 1–2mm. The efficacy of the attachment device was extremely successful. To date all parturients (n=102) studied were comfortable throughout the insertion and the testing period. There was no infection, bleeding or any significant local complication at any attachment site (n=204). No detachments occurred prior to 9cm. The system's in-vivo clinical trial generated individual partograms with real time dilatation and head station measurements. The measurements had accuracy of <5mm and had an agreement with a difference of up to 10mm with the clinical assessments in labor CONCLUSIONS: Previous cervicometers have failed for reasons such as: lack of head station measurements, cervix distortion by the sensors, poor attachment, cervical injury and inaccurate measurements. Our system provides accurate continuous measurements of dilatation and station. The method is superior to digital examination and provides real time diagnosis of non-progressive and precipitous labor. The system is likely to reduce discomfort and infections associated to multiple vaginal examinations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".