Abstract TP322: Towards better screening for Post Stroke Depression
Bibliographic record
Abstract
Post stroke depression (PSD), occurring in 33% of stroke survivors, is considered the most critical obstacle to rehabilitation after stroke. PSD is consistently linked with increased length of hospital stay, motivation to undergo rehabilitation, poorer rehabilitation outcomes, decreased engagement in recreational and social pursuits, decreased quality of life, and mortality. Although addressing PSD should be considered a critical factor in stroke recovery, the condition remains largely under-diagnosed. A challenge to diagnosis of PSD is a lack of appropriate screening tools. Existing screening tools were not designed for stroke populations or are too time consuming and complex to be utilized routinely. The goal of the present study was to identify a quick, two item screening tool for PSD that requires no training or scoring. Specifically, we examined what brief combination of dichotomized self-report questions can be used as indicators for a DSM-IV-TR classification of depression in stroke survivors In this prospective natural history study, a consecutive sample of 121 stroke survivors completed comprehensive interviews at 10 days post stroke. Contender questions assessing mood, cognition, physical functioning, social support, and relationship profiles were selected based on previous findings. At 10 days, 7 of 121 participants were classified as depressed in accordance with the SCID-I, the gold standard diagnostic tool for clinical depression. Results of logistic regression indicated that patients who met threshold on two MHI item, are you a happy person and have you been so down in the dumps, were 25 times more likely to be depressed than patients who did not (C Statistic > 0.90). As well, these brief questions better predicted PSD at 10 days than did the total MHI score. In the present paper, screening models are considered with regard to practical and theoretical issues in the assessment of PSD. The present study indicates that quick and simple screening tools designed for PSD can be used to select patients for diagnostic assessments and facilitate timely diagnoses of PSD. The present study contributes to our understanding of the clinical presentation of depression in the context of stroke and to our goal of facilitating recovery from stroke.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".