Routines for intrapartum fetal monitoring are important, but why and, more importantly, how are they used?
Bibliographic record
Abstract
Sir, Recently, Kaasen et al described clinical routines for intrapartum fetal monitoring in all Norwegian birth units.1 All units had a Pinard stethoscope, handheld Doppler and cardiotocography (CTG). Half of the units used ST waveform analysis (STAN), and 48% had access to fetal scalp blood sampling, most often for lactate analysis. Umbilical cord blood gas analyses were performed in 90% of the units, most often after high-risk deliveries. Some deviations from national recommendations were observed. Admission CTG was used routinely in low-risk women in 23% of the birth units. Use of intermittent auscultation of the fetal heart with a Pinard stethoscope, handheld Doppler or external CTG transducer differed among units, as did the time intervals between auscultation both in the first and second stage of labor in low-risk pregnancies. The definitions of slow progress of labor were also not consistent. Describing the routines for intrapartum fetal monitoring is not sufficient if they are not followed and not used for proper indications. Therefore, the knowledge of how often these routines are or are not followed in clinical practice and why, is of utmost importance. However, we have very little information on these, both at a national and international level. Admission CTG is not recommended in Norway in low-risk women as it is generally accepted that there is no evidence for its use.1-3 Nonetheless, it is routinely used in 23% of the birth units. Admission CTG is not recommended by the WHO, in the UK, Canada, Australia, New Zealand or Denmark.1 However, in Sweden, is it widely used. Why is it so? Recently, Swedish researchers discussed why they deemed admission CTG beneficial in a retrospective, hospital-based study including both low- and high-risk pregnancies, many with signs of placental abruption at admission.4 In my opinion, results from such a study do not justify their conclusions. On the other hand, Sweden does not use STAN technology, whereas many units in Norway do. Since the STAN algorithm is based on a modified, old version of the FIGO guideline for CTG interpretation, the new FIGO guidelines from 2015 were not introduced in Norway, as they are in Sweden in a slightly modified form. There is sufficient evidence that STAN is not superior to CTG alone or combined with fetal blood sampling for any important clinical outcomes.5 STAN can only be used after 36 weeks of gestation, only after rupture of membranes and has a complex algorithm; in addition many clinicians do not trust it to give a significant warning/alarm at an appropriate time. Thus, it is striking how traditions and beliefs, rather than evidence, continue to influence the method chosen for fetal monitoring. In Norway, the Pinard stethoscope appears to have been replaced by handheld Doppler or an external CTG probe.6 In my opinion, this is not well founded, since the Pinard stethoscope may be better at avoiding misidentification of maternal heart rate as a falsely reassuring fetal heart rate. There are too many court cases and claims for compensation after birth injury to the child where handheld Doppler or external CTG probe have produced a falsely reassuring fetal heart rate trace. Of course, if in doubt, a real-time ultrasound machine should be used. The publication by Kaasen et al1 should be followed by new studies evaluating why different methods for fetal monitoring are used, the rationale for the decision, and how these routines are followed and implemented in clinical practice.
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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 | 0.015 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".