Prescription of pharmacotherapy for depression in elderly people varies with age, race, gender, and length of care
Bibliographic record
Abstract
Strothers HS, Rust G, Minor P, et al . Disparities in antidepressant treatment in Medicaid elderly diagnosed with depression. J Am Geriatr Soc 2005;53:456–61.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What is the prevalence of drug treatment for depression in different populations of elderly people insured by Medicaid? ### ![Graphic][5] Design: Cross sectional study. ### ![Graphic][6] Setting: Medicaid claims data from five US states in 1998. ### ![Graphic][7] Population: 7339 people aged 65–84 years with an outpatient Medicaid claim for depression (international classification of diseases diagnosis). Exclusions: bipolar illness or dysthymia. ### ![Graphic][8] Assessment: Information about Medicaid recipients, including demographics, final action, paid claims, and days in long term care, was obtained from insurance claims data. National drug codes were used to identify prescriptions for antidepressants. Rates of drug treatment were calculated with 95% confidence intervals. Logistic regression was used to assess the effect of multiple factors on drug treatment. ### ![Graphic][9] Outcomes: Prevalence of antidepressant prescriptions. In elderly people with depression, 24% received no antidepressant treatment, 26.3% received old antidepressants alone or … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bthe%2BAmerican%2BGeriatrics%2BSociety%26rft.stitle%253DJ%2BAm%2BGeriatr%2BSoc%26rft.aulast%253DStrothers%26rft.auinit1%253DH.%2BS.%26rft.volume%253D53%26rft.issue%253D3%26rft.spage%253D456%26rft.epage%253D461%26rft.atitle%253DDisparities%2Bin%2Bantidepressant%2Btreatment%2Bin%2BMedicaid%2Belderly%2Bdiagnosed%2Bwith%2Bdepression.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1532-5415.2005.53164.x%26rft_id%253Dinfo%253Apmid%252F15743289%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=10.1111/j.1532-5415.2005.53164.x&link_type=DOI [3]: /lookup/external-ref?access_num=15743289&link_type=MED&atom=%2Febmental%2F8%2F4%2F117.atom [4]: /lookup/external-ref?access_num=000227189800014&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".