Rates of reporting suicidal ideation and symptoms of depression on Children’s Depression Inventory in a paediatric neurology sample.
Bibliographic record
Abstract
The Children’s Depression Inventory (CDI) is frequently used to screen for the symptoms of depression and suicidal thinking during psychological or neuropsychological evaluations. This includes assessment of children with neurological conditions who are at risk of experiencing suicidal thoughts due to general cognitive, psychiatric, and neurological deficits. The purpose of this study was to examine the prevalence and correlates of suicidal thinking and symptoms of depression in youth with neurological disorders, as measured by the CDI. We expected that reporting suicidal ideations would most often occur in children with epilepsy and individuals with low IQ, and positively correlate with impulsivity. Participants included 313 paediatric neurology patients (mean age = 13.1 years, SD = 3.1, range = 7–17) who underwent neuropsychological assessments, including completion of the CDI. Clinically elevated levels of symptoms of depression were found in 10 % of children, with 18.8 % of the total sample endorsing suicidal ideation on the CDI. Suicidal ideation was most commonly endorsed by youth with epilepsy (22.8 %), children between ages 7 and 10 years (25.8 %), youth with intellectual disabilities (40 % for IQ below the 2nd and 70 % for IQ below 0.2nd percentiles), and girls with attention problems (67 %). Depressive symptoms were significantly correlated with IQ, processing speed, executive functions, attention, parent-reported internalizing behaviours, and gender. Suicidal ideations were best predicted by low verbal intelligence, executive dysfunction, being female, and problems with inattention. Assessments of youth with neurological issues should include a psychological measure, particularly for patients with epilepsy and cognitive disabilities, even at a relatively early age.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".