Diagnoses of anxiety and depression in clinical-scenario patients: survey of Saskatchewan family physicians.
Bibliographic record
Abstract
OBJECTIVE: To investigate family physicians' differential diagnoses of clinical-scenario patients presenting with symptoms of either generalized anxiety disorder (GAD) or a major depressive episode (MDE). DESIGN: Cross-sectional survey. SETTING: Saskatchewan. PARTICIPANTS: A total of 331 family physicians practising in Saskatchewan as of December 2007. MAIN OUTCOME MEASURES: Type and number of physicians' differential diagnoses for a GAD-scenario patient and an MDE-scenario patient. RESULTS: The survey response rate was 49.7% (331 of 666 surveys returned). Most physicians suggested a diagnosis of anxiety (82.5%) for the GAD-scenario patient and a diagnosis of depression (84.2%) for the MDE-scenario patient. In descending order, the 5 most frequent differential diagnoses for the GAD-scenario patient were anxiety, hyperthyroidism, depression, panic disorder or attack, and bipolar disorder. The 5 most frequent differential diagnoses for the MDE-scenario patient were depression, anxiety, hypothyroidism, irritable bowel syndrome, and anemia. Neither a diagnosis of anxiety nor a diagnosis of depression was associated with physicians' personal attributes (sex, age, and years in practice) or organizational setting (number of total patient visits per week, private office or clinic, solo practice, Internet access, and rural practice setting). However, physicians in solo practice suggested fewer differential diagnoses for the GAD-scenario patient than those in group practice; physicians in practice 30 years or longer suggested fewer differential diagnoses for the MDE-scenario patient than those in practice fewer than 10 years. On average, physicians suggested 3 differential diagnoses for each of the scenarios. CONCLUSION: Most family physicians recognize depression and anxiety in patients presenting with symptoms of these disorders and consider an average of 3 differential diagnoses in each of these cases.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".