Sertraline with or without interpersonal psychotherapy reduces dysthymia symptoms over psychotherapy alone
Bibliographic record
Abstract
(2002) Journal of Affective Disorders 68, 317; Browne G, Steiner M, Roberts J et al. . Sertaline and / or interpersonal psychotherapy for patients with dysthymic disorder in primary care: 6-month comparison with longitudinal 2-year follow-up of effectiveness and costs. . April; . : . –30. . [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: For people with dysthymia, what are the long-term costs and effects of combining sertraline with interpersonal psychotherapy in primary care? Randomised 6 month trial with 18 month follow up (allocation concealed). The outcome assessor was blind to group allocation. Primary care university-affiliated Health Services Organisation in Ontario, Canada. 707 adults with DSM-IV dysthmic disorder. All were aged 18–74 years (mean 42 years; 68% female). People with a chronic or acute episode of a major depressive disorder were eligible. Exclusion criteria were previous sertraline use; hypersensitivity to selective serotonin reuptake inhibitors; acute suicide risk; depot neuroleptic drugs within 6 months; participation in other trials within 1 month; use of sertonergic drugs; primary or secondary diagnosis of a psychotic disorder; clinically significant or unstable medical conditions; pregnancy; lactation, and women of childbearing age not using reliable contraception or planning pregnancy within 25 months. Participants received 6 months of sertraline alone, interpersonal psychotherapy alone or sertraline plus psychotherapy. The initial dose of … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Baffective%2Bdisorders%26rft.stitle%253DJ%2BAffect%2BDisord%26rft.aulast%253DBrowne%26rft.auinit1%253DG.%26rft.volume%253D68%26rft.issue%253D2-3%26rft.spage%253D317%26rft.epage%253D330%26rft.atitle%253DSertraline%2Band%252For%2Binterpersonal%2Bpsychotherapy%2Bfor%2Bpatients%2Bwith%2Bdysthymic%2Bdisorder%2Bin%2Bprimary%2Bcare%253A%2B6-month%2Bcomparison%2Bwith%2Blongitudinal%2B2-year%2Bfollow-up%2Bof%2Beffectiveness%2Band%2Bcosts.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0165-0327%252801%252900343-3%26rft_id%253Dinfo%253Apmid%252F12063159%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/S0165-0327(01)00343-3&link_type=DOI [3]: /lookup/external-ref?access_num=12063159&link_type=MED&atom=%2Febmental%2F6%2F1%2F29.atom [4]: /lookup/external-ref?access_num=000176496500017&link_type=ISI
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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; both teacher heads agree on what is shown here.
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".