The acceptability of cervical electrical impedance spectroscopy within a multi-modal preterm birth screening package: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Reducing the rate of preterm birth is a cornerstone of global efforts to address child mortality, however existing screening tests offer imperfect prediction. Cervical electrical impedance spectroscopy (EIS) is a novel technique to quantify the ripening changes which precede labour. Mid-trimester EIS measurements have been shown to accurately predict preterm birth in asymptomatic women. This study aimed to comprehensively evaluate the acceptability of cervical EIS to low and high-risk women as part of a package of screening tests performed during a larger prospective trial. METHODS: In this parallel convergent mixed methods study, 40 women completed questionnaires before and after screening tests (EIS, cervical length measurement and fetal fibronectin quantification). Quantitative outcomes were anxiety levels before and after screening (Spielberger State Trait Anxiety Inventory, STAI-6), pain (Short Form McGill Pain Questionnaire) and ratings of EIS device appearance and test acceptability (visual analogue scales). Twenty-one women (11 high-risk, 10 low-risk) also attended a semi-structured qualitative interview. Interviews were recorded and transcribed, then thematic analysis was performed. A convergence coding matrix was constructed to enable triangulation of quantitative and qualitative results. RESULTS: High risk women demonstrated a significant reduction in anxiety following screening (mean STAI-6 score 34.5 vs. 29.0, p = 0.002). A similar trend was observed among low-risk participants. Ratings of pain, EIS device appearance and procedural acceptability did not differ between groups. Mean pain ratings were low (visual analogue scale 0.97 and 1.01), comparing favourably to published evaluations of conventional screening tests. Qualitative analysis provided insight into both the physical consequences and emotional experiences of screening. Additional determinants of the screening experience included device design, pre-existing perspectives on intimate examination, attitudes to knowledge in pregnancy and interaction with clinical staff. Finally, a range of practical considerations regarding wider use of EIS were identified, with valuable complementary detail regarding acceptability for use in antenatal care. CONCLUSIONS: Cervical EIS is well tolerated and acceptable to both low and high-risk women when performed as part of a multi-modal screening package. These results provide useful insights to inform the design of future study and screening protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".