Factors affecting the implementation and use of electronic templates for histopathology cancer reporting
Bibliographic record
Abstract
The surgical pathology report on cancer resection specimens is fundamental for providing clinicians with the information needed for adequate patient oncology treatment. Since the multi-institutional quality study on pathology reporting of colorectal cancer published by Zarbo in 1992,1 many other studies have shown that the use of checklists or synoptic reporting is superior to traditional narrative (free text) reporting.2–4 Using electronic health records, synoptic histopathology reporting tools can be designed to be very sophisticated with discrete data fields, drop down menus, and automated SNOMED encoding.5 The use of discrete data fields (’atomic data’) means that it is possible to automatically search, extract, and transmit data electronically.4 Despite the apparent benefits of electronic synoptic histopathology reporting, and the successful regional implementation of such a reporting system in Ontario, Canada,5 others have reported that the implementation and use of electronic histopathology reporting is no easy organisational task.6,7 Similar challenges have also been reported regarding the implementation and use of a web-based synoptic reporting tool for cancer surgery.8,9 From a management and organisational perspective, the list of possible causes for project failure with respect to information technology development, implementation and use is long.10 In our opinion, a pro-active understanding and management of key organisational issues is a requirement for successful long-term synoptic histopathology cancer reporting.
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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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".