Promoting Interest and Challenging Myths Regarding Training and Careers in Medical Renal Pathology: A Pathology Trainee Survey Analyzed
Bibliographic record
Abstract
The Renal Pathology Society (RPS) is an international, nonprofit, medical association with the generalized goal of improvement and dissemination of knowledge regarding the pathology and pathophysiology of renal disease. The tasks of the training committee include collecting and disseminating information about renal pathology training programs and encouraging their broad availability. In preparation to achieve these goals, the training subcommittee designed a 10-question, computerized survey to query pathology trainees with regard to their knowledge of, exposure to, potential interest in, and barriers to training in renal pathology. The survey was distributed to four training programs and there were 44 total responders. The vast majority of responders were senior pathology residents in academic institutions with up to three renal pathologists on faculty. The majority became aware of renal pathology as a subspecialty choice via exposure in their medical school pathology course. The majority were in training programs with a required renal pathology elective, but many only elected to participate in preparation for the board exam. When queried why responders would not consider renal pathology for a fellowship choice or career, three modifiable and two nonmodifiable barriers are summarized: nonmodifiable (“lack of interest/stronger interest in other subspecialties” and “limited job market”) and modifiable (“belief of limitation to academia,” “belief of lack of variety,” and “lack of early exposure”). Most pathology training programs have the resources available for adequate education in medical renal pathology. However, earlier exposure and more accurate information about the job market may lead to a better understanding of (and increased interest in) the field.
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 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 | 0.005 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".