Additional file 1 of A comparative analysis of pediatric mental health-related emergency department utilization in Montréal, Canada, before and during the COVID-19 pandemic
Bibliographic record
Abstract
Additional file 1: Table S1. MHR ED diagnoses included in the study sample. Table S2. Patient characteristics of included vs. excluded MHR ED visits. Table S3. Frequency of the number of MHR ED visits per unique patient. Table S4. Posterior estimates of the causal impact of COVID-19 on MHR ED visits, April 1, 2016 to November 30, 2021. Table S5. Semi-parametric GAM for MHR ED utilization, April 1, 2016 to November 30, 2021 (with age group as a dichotomous variable). Figure S1. MHR ED utilization at the CHU Sainte-Justine from April 1, 2016 to November 30, 2021 (by diagnostic category). Figure S2. Bayesian structural time series model (with raw data). Figure S3. Decomposition of additive time series. Figure S4. Original and deseasonalized time series. Figure S5. Graphical representation of the parametric and smooth terms included in the semi-parametric GAM
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.661 | 0.030 |
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".