Growth curves in short supply: a descriptive study of the availability and utility of growth curve data in adolescents with eating disorders
Bibliographic record
Abstract
BACKGROUND: Healthy body weight (HBW) determination affects multiple aspects of eating disorder (ED) treatment. For example, it can inform patients and providers as to when return of menses (ROM), an objective determinant of health, can occur. Growth curves (GCs) are sensitive indicators of health in youth and when up to date provide critical information regarding normal and expected trajectories of growth. Although not widely recommended as a first line tool for HBW calculation, a GC guides providers selecting a HBW that is individualized to each patient. The primary aim of this paper was to assess availability and feasibility of utilizing GC data for HBW prediction in adolescents referred for an ED assessment. We also sought to determine how this calculation compared to the standardized HBW calculation that uses mean body mass index (BMI) for age and how each of these numbers compared to the actual weight at ROM. METHODS: A retrospective chart review was completed on outpatients assessed for EDs between January 2004 and December 2006. A total of 102 patients met inclusion criteria. Demographic information, GC data, HBW predictions, and menstrual history were analyzed. A comparison of predicted HBWs using the aforementioned calculations and weight at ROM was performed using t-test analyses. RESULTS: Eighty-one patients (79.4%) had GC data available at assessment although HBW prediction was possible in only 24 patients (23.8%) due to poor GC completion. Of those 24 patients, 9 had ROM data available; no significant difference between our predicted HBW and the weight at ROM was found in these patients. In cases where HBW predictions could be compared using GC data and the BMI method, we found the GC calculation to be overall superior. CONCLUSIONS: We found overall rates of GC completion to be very low in our patients, which in turn limited the feasibility of relying on a GC for HBW calculation in ED patients. When complete, GCs provide accurate HBWs for most patients with EDs although it is clear that secondary methods of calculation are required given the gaps in data observed using this cohort.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".