Trial · NCT07880665
Machine Learning to Predict Fever After Labor Analgesia
NCT07880665, Not yet recruiting, Chengdu Jinjiang Maternity and Child Health Hospital
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. Machine Learning to Predict Fever After Labor Analgesia. Trials, record NCT07880665. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07880665
On this page
Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- Machine Learning to Predict Fever After Labor Analgesia
- NCT number
- NCT07880665
- Status
- Not yet recruiting
- Enrollment
- 6,144 participants, estimated
- Lead sponsor
- Chengdu Jinjiang Maternity and Child Health Hospital
- Conditions
- Intrapartum Fever
- Interventions
- Labor analgesia
- First posted
- 2026-10-19
- Last updated
- 2026-10-19
Source ClinicalTrials.gov
Registered outcomes3
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Post-labor Analgesia Intrapartum Fever | From initiation of labor analgesia through delivery, an average of 10 hours | Number of participants with PAIF, defined as maternal axillary temperature ≥38.0°C occurring after initiation of labor analgesia and before delivery. Axillary temperature was measured hourly during labor. A participant with at least one febrile measurement (≥38.0°C) after initiation of labor analgesia and before delivery was classified as having PAIF. Febrile events were confirmed through nursing charts and electronic health records. |
| Secondary | Area Under the Receiver Operating Characteristic Curve (AUROC) | Through study completion, an average of 1 year | AUROC of the ensemble machine learning model for predicting PAIF. The model outputs a predicted probability of PAIF; AUROC quantifies discrimination between participants with and without PAIF in the internal and external validation sets. |
| Secondary | Area Under the Precision-Recall Curve (AUPRC) | Through study completion, an average of 1 year | UPRC of the ensemble machine learning model for predicting PAIF. AUPRC summarizes the trade-off between precision and recall across classification thresholds in the internal and external validation sets. |
Source ClinicalTrials.gov
Outcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers0
The papers the trial lists on ClinicalTrials.gov.
None in this release.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07880665
GET https://conductscience.org/api/trials/v1/NCT07880665/registered