Frecuencia y pesquisa de síntomas en pacientes crónicos en fases avanzadas en un hospital clínico: ¿Existe concordancia entre pacientes y médicos?
Bibliographic record
Abstract
BACKGROUND: Physicians tend to over or underestimate symptoms reported by patients. Therefore standardized symptom scoring systems have been proposed to overcome this drawback. AIM: To estimate the prevalence and the diagnostic accuracy of physical and psychological symptoms and delirium in patients admitted to an internal medicine service at a university hospital. MATERIAL AND METHODS: We studied 58 patients, 45 with metastasic cancer and 13 with other advanced chronic diseases. The following scales were used: the Confusion Assessment Method for the diagnosis of delirium; the Edmonton Symptom Assessment Scale (ESAS) for pain and other physical symptoms; the Hospital Anxiety and Depression Scale to assess anxiety and depression. The ESAS was simultaneously applied to patients without delirium and their doctors to assess the level of diagnostic concordance. RESULTS: Twenty two percent of patients had delirium. Among the 45 patients without delirium, 11 (25%) had at least eight symptoms and 39 (88.6%) had four symptoms. The prevalence of symptoms was very high, ranging from 22 to 78%. Pain, restlessness, anorexia and sleep disorders were the most common. The concordance between symptoms reported by patients and those recorded by doctor was very low, with a Kappa index between 0.001 and 0.334. CONCLUSIONS: In our sample of chronic patients, there is a very high frequency of psychological and physical symptoms that are insufficiently recorded by the medical team.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".