Pain measurement as part of primary healthcare of adult patients with sickle cell disease
Bibliographic record
Abstract
OBJECTIVE: The aim of this exploratory, cross-sectional study was to evaluate pain in sickle cell disease patients and aspects related to primary healthcare. METHODS: Data were obtained through home interviews. The assessment instruments (body diagram, Numerical Pain Scale, McGill Pain Questionnaire) collected information on the underlying disease and on pain. Data were analyzed using the Statistical Package for Social Sciences program for Windows. Associations between the subgroups of sickle cell disease patients (hemoglobin SS, hemoglobin SC, sickle β-thalassemia and others) and pain were analyzed using contingency tables and non-parametric tests of association (classic chi-square, Fisher's and Kruskal-Wallis) with a level of 5% (p-value < 0.05) being set for the rejection of the null hypothesis. RESULTS: Forty-seven over 18-year-old patients with sickle cell disease were evaluated. Most were black (78.7%) and female (59.6%) and the mean age was 30.1 years. The average number of bouts of pain annually was 7.02; pain was predominantly reported by individuals with sickle cell anemia (hemoglobin SS). The intensity of pain (Numeric Pain Scale) was 5.5 and the quantitative index (McGill) was 35.9. This study also shows that patients presented a high frequency of moderately painful crises in their own homes. CONCLUSION: According to these facts, it is essential that pain related to sickle cell disease is properly identified, quantified, characterized and treated at the three levels of healthcare. In primary healthcare, accurate measurement of pain combined with better care may decrease acute painful episodes and consequently minimize tissue damage, thus improving the patient's overall health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".