A Gap Analysis Assessing the Perceptions of Primary Care Physicians in the Management of Kidney Recipients After Transplantation
Bibliographic record
Abstract
OBJECTIVES: To examine the practice patterns and perceptions of primary care physicians in the management of chronic diseases in kidney recipients, assess care provided to recipients, and identify barriers to the optimal delivery of primary care to recipients. METHODS: A self-administered questionnaire on the primary care of kidney recipients was developed and implemented. The survey investigated physician comfort and practice patterns in providing preventive and chronic care to recipients, patient self-management support, and physician perceptions on communication with transplant centers and barriers to ideal care. RESULTS: A total of 210 physicians completed the survey (response rate of 22%). Among the respondents, 73% indicated they were currently providing care to kidney recipients. The majority of physicians specified that they rarely (57%) or never (20%) communicate with transplant centers. Most physicians felt comfortable providing care to recipients for non-transplant-related issues (92.5%), vaccinations (85%), and periodic health examinations (94%). The majority (75.3%) of physicians felt uncomfortable managing the immunosuppressive medications of recipients. Physicians' most commonly stated barriers to delivering optimal care to recipients were insufficient guidelines provided by the transplant center (68.9%) and lack of knowledge in managing recipients (58.8%). Suggested resources by physicians to improve their comfort level in managing recipients included guidelines and continuing medical educational activities related to transplantation. CONCLUSIONS: Our results suggest that there are barriers to delivering optimal primary care to kidney recipients. The approach to providing resources needed to bridge the knowledge gap for physicians in the management of recipients requires further exploration.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".