Diagnostic accuracy and feasibility of depression screening in spinal cord injury: A systematic review
Bibliographic record
Abstract
Context: Individuals with spinal cord injury or disease (SCI/D) are at increased risk of depression, which is associated with poor short- and long-term outcomes. Accurate diagnosis is complicated by overlapping symptoms of both conditions, and a lack of consensus-derived guidelines specifying an appropriate depression screening tool.Objective: To conduct a systematic review to: (1) identify the diagnostic accuracy of established depression screening tools compared to clinical assessment; and, (2) to summarize factors that influence feasibility of clinical implementation among adults with SCI/D.Methods: A systematic search using MEDLINE, EMBASE, PsycINFO, CINAHL and the Cochrane databases using the terms spinal cord injury, depression or mood disorder, and screening or diagnosis identified 1254 initial results. Following duplicate screening, five articles assessing eight screening tools met the final inclusion and exclusion criteria. Measures of diagnostic accuracy and feasibility of implementation were extracted. The Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess study quality.Results: The Patient Health Questionnaire-9 (PHQ-9) had the highest sensitivity (100%), and specificity (84%). The 2-item version, the PHQ-2, comprised the fewest questions, and six of the eight tools were available without cost. Utilizing the QUADAS-2 tool, risk of bias was rated as low or unclear risk for all studies; applicability of the results was rated as low concern.Conclusion: The PHQ-9 is an accurate and feasible tool for depression screening in the adult SCI/D population. Future studies should evaluate the implementation of screening tools and the impact of screening on access to mental health interventions.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".