Resident education and quality of gross tissue examination practices of benign uteri
Bibliographic record
Abstract
INTRODUCTION: In the USA, most anatomical pathology residency training is based on an apprenticeship model in which residents learn directly by watching more senior personnel and then performing the examination. The level and the effect of the standardisation of resident trainee gross tissue examination practices have not been extensively evaluated. METHODS: In this apprenticeship-based training programme, a retrospective report review was performed to measure the level of standardisation of gross description (for 11 mandatory descriptors) and tissue submission (for four mandatory sections) practices for uterine specimens removed for benign conditions (n=78). Practices were examined for significant relationships with error, turnaround time (TAT), resource utilisation and postgraduate year of resident (n=25) training. RESULTS: Residents provided mandatory descriptors from 23.1% to 93.6% of the time and submitted mandatory sections from 82.1% to 96.2% of the time. Cases submitted by less experienced residents had a longer TAT and were associated with more errors, measured by the necessity to submit additional tissues. Less experienced residents used greater resources (submitting 9.5 tissue cassettes per case) compared with more experienced residents (7.3 cassettes per case), and a statistically significant correlation was found between the number of cassettes submitted and TAT. CONCLUSIONS: In this training programme, the model of apprenticeship training leads to less than optimal standardisation of gross examination practices, inefficiency, active errors and a high frequency of latent conditions leading to error.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".