The shrinking workforce of pathologists: implications for healthcare and possible solutions
Bibliographic record
Abstract
Dear Editor, I would like to draw your attention to a pressing issue that threatens the sustainability and effectiveness of pathological diagnostics in Italy: the alarming shortage of pathologists and the increasing workload imposed on the remaining specialists, which significantly affects diagnostic turnaround times, a critical aspect of patient care. This situation could compromise service efficiency and raise concerns about diagnostic accuracy and patient safety. Recent projections indicate a growing deficit of medical specialists across various disciplines, with pathology being one of the most affected. According to workforce planning data, the number of active pathologists in Italy is expected to decline significantly by 2025 due to an aging workforce and an insufficient number of newly trained specialists 1. Moreover, many residency scholarships remain unfilled each year, as pathology remains an unpopular choice among medical graduates. For example, in 2024 alone, 110 out of 180 (52%) residency positions in pathology were left unassigned 2. While this high percentage may be partially attributed to a general shortage of new medical graduates, it also suggests a declining interest in pathology as a career choice, with many students preferring other disciplines.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".