A patient and primary care perspective: a patient's perspective on the treatment of depression.
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is common, chronic, and associated with significant functional impairment. It is highly treatable. It is therefore not only a major public health problem but also one that provides a unique opportunity in medicine to make a significant difference. This article will discuss the methodology needed to demonstrate empirically the impact of treatment on actual burden of illness in practice. Where efficacy studies demonstrate whether a treatment can work, effectiveness studies tell us whether they actually do work. Clinical trials exclude incompetent, non-compliant, and seriously comorbid patients, so that the information obtained from these trials tells us the most about the patients we see the least. Small differences in effect size in pivotal trials of efficacy have become a key variable for rating treatments as first line or second line, without consideration of effectiveness variables such as comorbidity, difficulty with appetite or sleep, patient preference, capacity for compliance, timing of functional impairment, and substance use. These effectiveness variables are less well studied, but critical to clinical decision making. In reality, fewer than 10% of our patients comply with and persist with treatment. To learn more about why patients are discontinuing treatment, we need to explore measures of effectiveness empirically. Effectiveness studies are also important to provide regulatory bodies with the data they need to balance the risk of treatment with the risk of failing to treat. Practical clinical trials and naturalistic follow-up studies will allow us to evaluate the true clinical impact of short-term efficacy trials.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".