Diagnoses during follow-up of patients presenting with fatigue in primary care
Bibliographic record
Abstract
BACKGROUND: Little is known about the distribution of diagnoses that account for fatigue in patients in primary care. We evaluated the diagnoses established within 1 year after presentation with fatigue in primary care that were possibly associated with the fatigue. METHODS: We conducted a prospective observational cohort study with 1-year follow-up. We included adult patients who presented with a new episode of fatigue between June 2004 and January 2006. We extracted data on diagnoses during the follow-up period from the patients' medical records as well as data on pre-existing chronic diseases. RESULTS: Of the 571 patients for whom diagnostic data were available, 268 (46.9%) had received one or more diagnoses that could be associated with fatigue. The diagnoses were diverse and mostly included symptom diagnoses, with main categories being musculoskeletal (19.4%) and psychological problems (16.5%). Clear somatic pathology was diagnosed in 47 (8.2%) of the patients. Most diagnoses were not made during the consultation when fatigue was presented. INTERPRETATION: Only a minority of patients were diagnosed with serious pathology. Half of the patients did not receive any diagnosis that could explain their fatigue. Nevertheless, because of the wide range of conditions and symptoms that may explain or co-occur with the fatigue, fatigue is a complex problem that deserves attention not only as a symptom of underlying specific disease.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".