To Screen, or Not to Screen, that is Depression
Bibliographic record
Abstract
of stay (LOS) (1 hour or less, 2-5 hours, 6 hours or more), and substance-related diagnosis.Variables are compared between each quarter using a generalized linear model.Results: There were 938 visits total during this time (467 male, 467 females, 4 missing).598 were Hispanic, 274 non-Hispanic White, 147 Native American, 45 Black, 8 Asian, 4 NH/PI, and 146 declined or unknown.The vast majority of visits were in adolescents 15-17yrs old.The most common diagnosis was cannabis-related disorder at 306 encounters, followed by alcohol n=303.The trajectory of visits from July 2019-March 2022 showed a decline from 98 visits in July-Sept 2019 to 51 visits in April-June 2020, followed by increase to 102 visits in Jan-Mar 2022.Comparisons of equivalent quarters for each year were as follows: Q1 ( 2020 n=71; 2021 n=71, 2022 n=102). Q2 (2020 n=51; 2021 n=81). Q3 (2019 n=98; 2020 n=75; 2021 n=107, 2019-2021). Q4 (2019 n=90; 2020 n=57; 2021 n=111).There were fewer female visits prior to onset of COVID-19 (n=40 in females vs n=58 in males in 2019 Q3) and decreased further early in the pandemic (N=29 vs 46 in males in 2020 Q3), but then rose more rapidly than males (n=59 female, n=48 male, 2021 Q3).The proportion of visits with LOS 5 hours in Q3 initially decreased from 27.8% of visits(n=25) in 2019 to 19.3% (n=11) in 2020, then increased significantly to 35.1% in 2021 (n=39).There was not a significant effect of other variables.Conclusions: The COVID-19 pandemic resulted in a rapid decrease in ED substance-abuse pediatric presentations, which rebounded to levels greater than pre-COVID.Females increased more than males.Visits with longer LOS increased during later pandemic.Future work includes understanding how mental health comorbidities and other socioeconomic stressors may relate to these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".