Die fetale Herzfrequenzvariation in der Magnetokardiografie und der Kardiotokografie - direkter Vergleich beider Verfahren
Bibliographic record
Abstract
BACKGROUND: Heart rate variability (HRV) is becoming increasingly important in the analysis of prepartal cardiotocography (CTG). Dawes and Redmann have developed a computer algorithm which can calculate short-term variability on the basis of CTG data. In dealing with artefacts, CTG monitors average heart rate values over several beats which makes the use of standard measures of HRV such as the root mean square of successive differences (RMSSD) inappropriate. Fetal magnetocardiography (FMCG) enables the registration of signals similar to the electrocardiogram and this permits the precise determination of heart beat duration and, in consequence, of measures of fetal HRV. METHODS: In this study we applied both methods--CTG and FMCG--sequentially and simultaneously in healthy pregnancies. Fetal short-term HRV was estimated on the basis of RMSSD values for both methods. RESULTS: The RMSSD values of the FMCG data were generally higher and showed a wider dynamic range than those of the CTG. The direct comparison of the simultaneously acquired data demonstrated that the data processing of the CTG signal leads to a suppression of essential aspects of short-term HRV. CONCLUSION: FMCG permits a substantially more differentiated examination of fetal HRV and offers new possibilities in the analysis of fetal cardiac activity.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".