Análise da intensidade, aspectos sensoriais e afetivos da dor de pacientes em pós-operatório imediato
Bibliographic record
Abstract
OBJECTIVE: To evaluate the pain of patients in the immediate postoperative period during admission, an hour after admission, and at discharge of the post-anesthesia care unit in terms of intensity, and sensory and affective aspects. METHODS: Analytical, cross-sectional study with 336 patients. Data were collected using a sociodemographic and clinical form, the Numeric Pain Rating Scale, and the short-form McGill Pain Questionnaire. Data collection occurred from September to October 2015 at the post-anesthesia care unit of a general hospital in the north-west of Rio Grande do Sul, Brazil. The significance level of the descriptive and statistical analyses was set at p<0.05. RESULTS: According to the data, 57.3% of the patients did not report pain and 47% felt pain from admission to discharge. Patients submitted to cancer and trauma surgeries reported more pain (p<0.01). At admission and maintenance, there was a prevalence of moderate and intense pain, and at discharge, a predominance of mild and moderate pain. CONCLUSIONS: The results showed a high percentage of patients with pain in the immediate postoperative period from admission to discharge. These findings can encourage researchers and health workers to conduct further investigations with the larger number of patients to allow for inferences.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".