Thematic analysis of the medical records of patients evaluated for kidney transplant who did not receive a kidney
Bibliographic record
Abstract
BACKGROUND: A potential pitfall of policies intended to promote referral for kidney transplant is that greater numbers of patients may be evaluated for transplant without experiencing the intended benefit of receiving a kidney. Little is known about the potential implications of this experience for patients. METHODS: We performed a thematic analysis of clinician documentation in the electronic medical records of all adults at a single medical center with advanced kidney disease who were referred to the local transplant coordinator for evaluation between 2008 and 2018 but did not receive a kidney. RESULTS: 148 of 209 patients referred to the local kidney transplant coordinator at our center (71%) had not received a kidney by the end of follow-up. Three dominant themes emerged from qualitative analysis of documentation in the medical records of these patients: 1) Forward momentum: patients found themselves engaged in an iterative process of testing and treatment that tended to move forward unless an absolute contraindication to transplant was identified or patients disengaged; 2) Potential for transplant shapes other medical decisions: engagement in the transplant evaluation process could impact many other aspects of patients' care; and 3) Personal responsibility and psychological burden for patients and families: clinician documentation suggested that patients felt personally responsible for the course of their evaluation and that the process could take an emotional toll on them and their family members. CONCLUSIONS: Engagement in the kidney transplant evaluation process can be a significant undertaking for patients and families and may impact many other aspects of their care. Policies to promote referral for kidney transplant should be coupled with efforts to strengthen shared decision-making to ensure that the decision to undergo transplant evaluation is framed as an explicit choice with benefits, risks, and alternatives and patients have an opportunity to shape their involvement in this process.
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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 | 0.036 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".