“Some Person Behind a Desk Is Going to Be Looking at My File”: Thematic Analysis of the Health Records of a National Sample of Patients with Advanced Kidney Disease Evaluated for Kidney Transplant
Bibliographic record
Abstract
Background: To be considered for kidney transplant, patients with advanced kidney disease must participate in a formal evaluation and selection process. Little is known about how this process unfolds in real-world clinical settings. Methods: We conducted a thematic analysis of clinician documentation related to the kidney transplant evaluation in the VA-wide electronic medical records of patients who were referred to a transplant center among a random sample of 4,000 adults with advanced kidney disease between 2004 and 2014 who were followed through 2019. Results: We identified 211 patients (5.2%) who were referred to a VA transplant center during follow-up. Four dominant themes emerged from qualitative analysis of clinician documentation in the electronic medical records of these patients: 1) far-reaching and inflexible medical evaluation: patients were expected to complete a demanding evaluation that could take a substantial physical and emotional toll on them and their family members, made little accommodation for their individual needs, and impacted many other aspects of their care; 2) psychosocial valuation: the psychosocial transplant assessment could be subjective and intrusive and placed substantial demands on patients' family members; 3) surveillance over compliance: clinicians monitored patients' adherence to a wide range of medical recommendations; 4) disempowerment and lack of transparency: patients had a strong desire to receive a transplant, but neither they nor their local clinicians had a clear understanding of what to expect from the evaluation process or the rationale for selection decisions, which left patients and their clinicians with little choice but to adhere to the transplant center's recommendations. Conclusions: To be considered for kidney transplant, patients had little choice but to engage in a rigid, demanding, and opaque evaluation process over which neither they nor their local clinicians had much control. These findings call for a more evidence-based, transparent, and individualized approach to the kidney transplant evaluation process. Funding: Veterans Affairs Support
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".