© 2009 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Fatigue and general practitioners Nijrolder and colleagues described the diagnoses they found during follow-up of patients presenting with fatigue in pri-mary care.1 Our study (prospective, cross-sectional, within a one-year period2) was performed to determine accompanying reasons for the encounter, symptoms, diagnostic procedures, recent diagnoses and therapeutic procedures in patients suffering from fatigue in a pri-mary care setting. Fatigue was associated with acute infectious diseases of the res-piratory tract, anemia, mental disorders, heart and circulation problems and nephropathies. The low rate of diagnoses at the initial consultation stated by Nijrolder and colleagues may be due to a watchful waiting strategy. Our investiga-tion revealed that about 70 % of the patients with fatigue were asked to see their general practitioner again. Watchful waiting seems to be a proper strategy after excluding cardiovascular problems and severe infections. This is supported by earlier investigations of Kenter and colleagues,3 who found that fatigue is often a temporary story.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.762 | 0.556 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".