ArticleF1000Research2022
Curating maternal, neonatal and child health (MNCH) datasets from a hospital's catchment area in Nigeria between 2014 and 2019.
Article in F1000Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this Data Note, we present details regarding Maternal, Neonatal, and Child Health (MNCH) datasets sourced directly from patients' medical records. These datasets consist of 538 maternal, 720 neonatal, and 425 child records, all collected at St Luke's General Hospital in Anua, Uyo, Nigeria, spanning from 2014 to 2019. Variables included in the datasets are: Maternal {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, symptom, prescription, blood pressure (mm Hg), temperature (degree centigrade), weight (Kg), latitude, longitude, elevation, (MSL), date record, GPS Accuracy (m)}; Neonatal {patient number, date of visit, gender, age, class of patient, address, LGA, symptom, health status, height (cm), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS Accuracy (m)}; and Child Health {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, health history, temperature (degree centigrade), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS accuracy (m)}. The purpose of sharing these datasets is to provide a resource for researchers interested in their potential reuse, whether for analysis, research, quality assurance, policy formulation, decision-making, patient safety, or other purposes. The datasets also include location information obtained through GPS (Global Positioning System) data from the study area, facilitating spatiotemporal analysis. We outline the methods used for curating the datasets, including the protocol for selecting and processing variables. To protect patient privacy, certain personal details such as names were replaced with unique patient numbers generated using Microsoft Excel. Furthermore, specific patient information, including addresses/locations, date of visit, latitude, longitude, elevation, and GPS accuracy, has been restricted for privacy reasons. Readers interested in accessing restricted data can make a formal request to the corresponding author (see data restriction statement). The curated datasets are available at the Open Science Framework.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.