Research Article Attitudes toward Management of Sickle Cell Disease and Its Complications: A National Survey of Academic Family Physicians
Bibliographic record
Abstract
Copyright © 2015 Arch G. Mainous III et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. Sickle cell disease (SCD) is a disease that requires a significant degree of medical intervention, and family physicians are one potential provider of care for patients who do not have access to specialists. The extent to which family physicians are comfortable with the treatment of and concerned about potential complications of SCD among their patients is unclear. Our purpose was to examine family physician’s attitudes toward SCD management.Methods. Data was collected as part of the Council of Academic Family Medicine Educational Research Alliance (CERA) survey in the United States and Canada that targeted family physicians who were members of CERA-affiliated organizations. We examined attitudes regarding management of SCD. Results. Overall, 20.4 % of respondents felt comfortable with treatment of SCD. There were significant differences in comfort level for treatment of SCD patients depending on whether or not physicians had patients who had SCD, as well as physicians who had more than 10 % African American patients. Physicians also felt that clinical decision support (CDS) tools would be useful for treatment
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".