Incidence and Control of Symptoms at the End of Life in Cancer Patients
Bibliographic record
Abstract
Background: at the end of life, the patient with cancer conditions presents various physical, emotional and spiritual symptoms. Palliative medicine allows a continuous and comprehensive treatment for the diagnosis and control of symptoms. Objective: to describe the incidence of symptoms according to the location of the initial tumor and its transition in the last stage of the disease. Method: a descriptive, longitudinal, prospective study of 100 terminally ill patients treated at the Institute of Oncology and Radiobiology was carried out between September 2017 and September 2019. The medical history record with two evaluations was taken as a source, using Edmonton Symptom Rating Scale, modified. A questionnaire was developed using the in-depth interview technique to collect information on symptoms. With the information, a database was made in Microsoft Excel 16.0 and they were processed using the SPSS-PC statistical package in version 19.0.1 for Windows, which made it possible to make tables and graphs. Results: the incidence of nine symptoms is described, the main ones: pain, fatigue, loss of appetite, anxiety and depression, independent of the anatomical structure affected by the primary tumor. A higher incidence of pain was found in general (78 %). During the final stage, the most frequent symptoms were: fatigue, anxiety, loss of appetite and dyspnea. Conclusion: the symptoms in terminal patients with cancer diseases are multiple and variable, sometimes closely related to the natural history of their disease. Symptomatic diagnosis and control requires recognizing needs and generating collective strategies to minimize suffering.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".