The needs of medical care and the quality of care in cancer patients, experiencing pain
Bibliographic record
Abstract
SUMMARY AIM OF THE STUDY. To evaluate the needs of medical care and the quality of care in cancer patients, experiencing pain. METHODS. One hundred cancer patients experiencing pain were interviewed in Kaunas Medical university Oncology hospital and Oncology department of Kaunas Medical university hospital (KMUH).. Age range- 27-68 years. Two questionnaires were used for the interview: “Pain anamnesis” and “McGill’s pain questionnaire”- specific questionnaire of pain-words. RESULTS. The results of questionnaires were analyzed. Men more often than women experienced chronic pain (17% and 48 %). There was statistically positive correlation between the strongest pain in 24 hours and weakest pain in 24 hours experience. While analyzing adverse reactions of various treatment methods in the respondents treated in KMUH Oncology department and KMU Oncology hospital the anorexia (p ≤ 0,05), constipation and insomnia were more common in KMUH (p ≤ 0,05). The quality of care according to 58% of Patients in KMUH and 34% in KMU OH was not sufficient (p ≤ 0,05). About 12% of KMUH patients and 46% in KMU OH declared that the quality of care is worse during the weekends. (p ≤ 0,05) CONCLUSIONS. A lot of adverse reactions appear while treating cancer patients with pain. One can decrease or even eliminate these reactions by purposeful care activities and providing information needed. Increasing the quality of care, the evaluation of received care by cancer pain patients will become better.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".