NÍVEIS DA DOR EM MULHERES COM CÂNCER DA CIDADE DE BELÉM-PA.
Bibliographic record
Abstract
The survey was to assess the levels of pain in women with cancer of the city of Belem-PA. It was realized at interview form. The sample was composed by 35 patients enrolled and serviced by AVAO- Voluntary Association to Support the Oncology, of the town of Belem-PA, females, with average age of 56,28 (± 8.68) years, diagnosed with the following types: breast, 46%; cervix, 37%; and other types of cancer, 17%. To conduct the research, it was used the question number 9 of the European Organization Research and Treatment of Cancer Questionnaire of Quality of Life (EORTC QLQ-c-30), which consists in the following question: during the last week, did you have pain?. This issue has four possible answers, a Likert scale type,of 4 scores(i.e.: no- score 1 ; less- score 2; moderately- score and much- score 4).83% of patients reported that have pain, because of the treatment and the disease itself, in the week before the interview, with answers distributed this way: 31% marked the option so much– score 4; 29% marked the option ' moderately – score 3; and 23% scored a little – score 2. Only 17% marked the answer No – score 1, affirming that they have no pain in the week preceding the interview.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".