An open-label study of citalopram for major depression following traumatic brain injury
Bibliographic record
Abstract
Major depression is associated with substantial psychosocial dysfunction and post-concussive symptomatology following traumatic brain injury (TBI). Studies to date of anti-depressant treatment for major depression post-TBI have been limited by small sample size. The goal of the present study is to examine the rates of response and remission associated with citalopram treatment for major depression following traumatic brain injury. Subjects with major depression following mild-to moderate TBI were treated with open-label citalopram with a starting dose of 20 mg/day to a maximum of 50 mg/day for either 6 weeks (n = 54) or 10 weeks (n = 26). The Hamilton Depression Rating Scale (HAMD) was used to assess depression severity. Response was defined by a 50% reduction in HAMD score, and remission was defined by a HAMD score of < or =7. The mean HAMD at baseline and 6 weeks were 23.66 (SD 6.8) and 16.30 (SD 9.3), respectively (t[53] = 7.157, p < 0.0001). The mean HAMD at 10 weeks was 12.96 (SD 7.9) (t[25] = 7.323, p < 0.0001). At 6 weeks, 54 subjects were assessed and 27.7% responded with 24.1% in remission. At 10 weeks, 26 subjects were assessed and 46.2% responded with 26.9% in remission. The response rate in the present sample was substantially lower than previously reported for patients with TBI, but comparable to the results of the largest effectiveness trial of citalopram for general out-patients with major depression in the absence of TBI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".