Psychosocial diagnoses occurring after patients present with fatigue.
Bibliographic record
Abstract
OBJECTIVE: To discover the frequency of psychosocial and other diagnoses occurring at the end of a visit when patients present to their FPs with concerns about fatigue. DESIGN: Cross-sectional study of patient-FP encounters for fatigue. SETTING: Ten FP practices in southwestern Ontario. PARTICIPANTS: A total of 259 encounters involving 167 patients presenting to their FPs between March 1, 2006, and June 30, 2010, with concerns about fatigue. MAIN OUTCOME MEASURES: The frequency of psychological and social diagnoses made at the end of visits, and whether diagnoses were made by FPs at the end of the visits versus whether the code for fatigue remained. The associations between patient age, sex, fatigue presenting with other symptoms, or the presence of previous chronic conditions and the outcomes was tested. RESULTS: Psychosocial diagnoses were made 23.9% of the time. Among psychosocial diagnoses made, depressive disorder and anxiety disorder or anxiety state were diagnosed more often in women (P = .048). Slightly less than 30% of the time, the cause of patients' fatigue remained undiagnosed at the end of the encounter. A diagnosis was made more often in men. CONCLUSION: Causes of fatigue frequently remain undiagnosed; however, when there is a diagnosis, psychosocial diagnoses are common. Therefore, it would be appropriate for FPs to screen for psychosocial issues when their patients present with fatigue, unless some other diagnosis is evident. Depression and anxiety could be considered particularly among female patients with fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".