A survey of birthweight percentile curves used in hospitals across Ontario
Bibliographic record
Abstract
This study was supported by a grant from the Canadian Institutes of Health Research. Dr Ray holds a Canadian Institutes of Health Research Chair in Reproductive and Child Health Services and Policy Research. No funding bodies had any role in the study design, data collection, analysis, decision to publish or preparation of the manuscript. The practice of weighing newborns enables the distinction between healthy weight babies and those who are either small for gestational age (SGA) or large for gestational age (LGA). Because SGA and LGA newborns experience greater morbidity and mortality, such categorization potentially influences clinical care and the expectations of the child’s parents (1). There is evidence that, relative to non-SGA infants, those classified as SGA have significantly longer stays in neonatal intensive care units, extended use of continuous positive airway pressure ventilation, and more use of supplemental oxygen (2). In other words, correctly classifying an infant as SGA or LGA, or neither, is important. Birthweight percentile curves (or ‘charts’) are the classic method to visually determine whether a newborn is SGA, LGA, or of appropriate weight for gestational age and sex; however, it is not clear whether newborn centres use the same type of chart. Therefore, we surveyed all hospitals in Ontario that provide newborn care to determine which newborn percentile curve they use in their centre. In 2013, a survey was e-mailed to all 99 hospitals in Ontario that perform inpatient obstetrical deliveries or inpatient neonatal care. Managers/directors of newborn nurseries and neonatal intensive care units were asked to provide the name(s) of the newborn birth-weight percentile chart(s) they use, as well as a mailed or e-mailed copy of the chart(s). A second request was e-mailed to nonresponders three months later. All hospital identifiers were removed after the survey results had been entered into a spreadsheet (Excel, Microsoft Corporation, USA). A total of 85 hospitals responded to the survey, corresponding to a survey response rate of 85.9% (Figure 1). Birthweight percentile curves used by 85 Ontario hospitals that provide newborn care. Data presented as a proportion (95% CI) and number of all respondent hospitals that use a particular curve. The dark grey column reflects the 48 hospitals (56.5%) that do not use a curve, and the light grey columns reflect the centres that reported using a specific curve. *The sum of the percentage of hospitals is slightly more than 100% because seven centres reported using two birthweight curves. CDC Centers for Disease Control and Prevention; CPEG Canadian Pediatric Endocrine Group; IHDP Infant Health and Development Program A total of 48 hospitals (56.5% [95% CI 45.9% to 66.5%]) reported not using any type of birthweight curve. Some reasons for nonuse were the low number of annual births, the absence of a nursery or that it was a level I facility. The remaining 37 centres reported using a birthweight curve, including seven centres that used two different curves (Figure 1) (3–9). The Fenton Fetal-Infant Growth Chart for Preterm Infants (3) was the most common birthweight curve used (20 centres [23.5%]; 95% CI 15.7% to 33.6%). Although births in Ontario comprise 37% of all births in Canada (10), birthweight percentile curves are underused in this province. Lacking an objective measure of an infant's birthweight percentile not only overlooks its potential SGA or LGA status, but also the inherent health consequences of being SGA or LGA and the need for growth monitoring thereafter (1). We also found there to be high variability in the birthweight curves used in Ontario. In the present study, 48 hospitals reported not using any curve, which is concerning because many of these facilities contribute to the care of many newborns in Ontario. To be clinically useful, a curve must reflect the current population of newborns; thus, because newborn weight percentile values have increased in Canada over time (11), some curves may be outdated. In addition, birthweight curves must be derived from a sample size large enough to sufficiently represent the population to which they will be applied. Finally, given that Canada's population is ethnically diverse and that nearly 35% of infants are born to immigrant women, a potential need for ethnic-specific curves has been expressed, although this is a topic of ongoing debate (12,13). Some of the curves used by survey respondents are potentially unsuitable for assessing newborn weight. The Centers for Disease Control and Prevention (CDC; Atlanta, USA) charts, for example, were developed using outdated data that did not include direct birth-weight measurements (6). Therefore, all hospitals that deliver babies and/or provide newborn care should use an endorsed birth-weight curve. We are part of a national team of clinical and policy experts whose goal is to ensure that standardized birthweight curves are used across Ontario and Canada (http://webapps.cihr-irsc.gc.ca/cris/detail_e?pResearchId=4508543&p_version=CRIS&p_language=E&p_session_id=1333597). The results of the current study inform our team about the need to disseminate standardized birthweight curves to all newborn units and providers in Ontario. This will be performed in partnership with Ontario's Provincial Council for Maternal and Child Health and affiliated groups. In future studies, our partners in other Canadian provinces should determine whether the pattern of use of newborn birthweight percentile charts is similar to that in Ontario.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".