Survey on synoptic reporting of pathology within the Center of Integrative Oncology Aachen-Cologne-Bonn-Düsseldorf (CIOABCD) and documentation of national and international chances for further optimization
Bibliographic record
Abstract
In 2008, the governmental organisation Palga Foundation started to introduce a system for pathology reports called "Synoptic Reporting". It aims to standardise pathology reports for, ideally, all types of pathology laboratories or institutes in a given geographical region. By introducing this system, Palga has significantly changed the field of pathology reporting in the Netherlands. Palga as an organisation is now at the centre of the data flow between pathology laboratories, cancer registries and research projects. By implementing a common software tool for the creation and processing of pathology reports, a huge database has been developed in the Netherlands, managed by Palga. This enables fast and selective data transfer, data analysis and the basis for research projects for Palga, cancer registries and researchers. In the study "The effects of implementing synoptic pathology reporting in cancer diagnosis: a systematic review" by Sluijter, Caro E. (1), it was shown that the introduction of SR has brought enormous benefits to pathology on a large scale. The effectiveness and progress of SR is based on a classification called the "Ontario classification" (see chapter 3.3). The purpose of this study was to compare the structures of the Netherlands with those of the recently established German CIOABCD structure in order to develop meaningful proposals to enable and facilitate progress towards standardisation and interoperability in Germany. In contrast to the Netherlands, SR reporting has not yet been implemented in the CIOABCD NRW. This was determined through structured interviews with representatives of the sites, including those of the NRW Cancer Registry. It was found that all representatives of the CIOABCD rely on NR-based structures. Individual institutes have made their own progress in the area of NR, so that structures ranging from free text to table format can be found. (see chapter 4.1). There was no evidence of measures to introduce common templates or infrastructures. Future-oriented projects were reported, e.g. a software tool called "Meldeportal", which aims to create a common interface for data transmission to the LKR-NRW. Another example is the plan to set up a database managed by the CIOABCD in which all members are to store data (see Chapter 6). A direct comparison between the two geographical regions with similar populations (Netherlands and NRW) is not directly feasible for a variety of reasons. Nevertheless, the comparison helps to suggest steps for changes towards SR. In line with this objective, several suggestions and recommendations were made to support the transition from NR to higher quality reporting in a step-by-step process.
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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 | 0.166 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.037 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".