External quality assurance as a revalidation method for pathologists in pediatric histopathology: Comparison of four international programs
Bibliographic record
Abstract
AIM: External quality assurance (EQA) is an extremely valuable resource for clinical pathologists to maintain high standards, improve diagnostic skills, and possibly revalidate medical license. The aim of this study was to participate in and compare four international slide survey programs (UK, IAP-Germany, USA-Canada, Australasia) in pediatric histopathology for clinical pathologists with the aim to use it as a revalidation method. METHODS: The following parameters were evaluated: number of circulations per year, number of slides, membership requirement, proof of significant pediatric pathology work, open to overseas participants, laboratory accreditation, issue of continuing professional development certificates and credits, slides discussion meeting, use of digital images, substandard performance letter, and anonymity of responses. RESULTS: The UK scheme, which has sampling procedure over several time frames (2 circulations/year, 30 slides), partial confidentiality, and multiple sources of data and assessors, can be used as a model for revalidation. The US-Canadian and Australasian schemes only partially fulfill the revalidation requirements. The IAP scheme appears to be essentially an educational program and may be unsuitable for revalidation. CONCLUSION: The purposes and programs of EQA schemes vary worldwide. In order for it to be used for revalidation, it is advisable that EQA schemes are immediately unified.
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 | 0.083 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".