Performance of residents using digital images versus glass slides on certification examination in anatomical pathology: a mixed methods pilot study
Bibliographic record
Abstract
BACKGROUND: It is anticipated that many licensing examination centres for pathology will begin fully digitizing the certification examinations. The objective of our study was to test the feasibility of a fully digital examination and to assess the needs, concerns and expectations of pathology residents in moving from a glass slide-based examination to a fully digital examination. METHODS: We conducted a mixed methods study that compared, after randomization, the performance of senior residents (postgraduate years 4 and 5) in 7 accredited anatomical pathology training programs across Canada on a pathology examination using either glass slides or digital whole-slide scanned images of the slides. The pilot examination was followed by a post-test survey. In addition, pathology residents from all levels of training were invited to participate in an online survey. RESULTS: A total of 100 residents participated in the pilot examination; 49 were given glass slides instead of digital images. We found no significant difference in examination results between the 2 groups of residents (estimated marginal mean 8.23/12, 95% confidence interval [CI] 7.72-8.87, for glass slides; 7.84/12, 95% CI 7.28-8.41, for digital slides). In the post-test survey, most of the respondents expressed concerns with the digital examination, including slowly functioning software, blurring and poor detail of images, particularly nuclear features. All of the respondents of the general survey (n = 179) agreed that additional training was required if the examination were to become fully digital. INTERPRETATION: Although the performance of residents completing pathology examinations with glass slides was comparable to that of residents using digital images, our study showed that residents were not comfortable with the digital technology, especially given their current level of exposure to it. Additional training may be needed before implementing a fully digital examination, with consideration for a gradual transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".