Analysis of Addenda in Anatomic Pathology as a Quality Monitoring Initiative
Bibliographic record
Abstract
CONTEXT: Along with the integration of immunohistochemical markers and molecular techniques into routine practice, addenda in surgical pathology reporting have not only increased in frequency but also evolved to include prognostic and therapeutic information. Because of the lack of uniform practice with respect to issuing addenda, information that can significantly change the diagnosis, prognosis, or treatment plan may be issued as an addendum as opposed to an amendment. OBJECTIVE: To audit addenda and identify instances of amendments masquerading as addenda. DESIGN: All addenda during a 36-month period were reviewed. Each addendum report was classified by accession class, issuing pathologist, subspecialty category, indication for addendum, whether the addendum constituted a change in diagnostic meaning, whether a change in prognosis occurred, and if a change in treatment plan was necessary. RESULTS: All cytology and autopsy addenda were deemed appropriate. Thirty-three of 5028 (6.5 of 1000) surgical pathology addenda were deemed to have changes: Among the 33 faux addenda, 30 (91%) contained supplemental diagnostic information that would alter patient management and 31 (94%) contained additional information that would change the prognosis from that entailed by the original diagnosis. CONCLUSIONS: Our study demonstrates that not infrequently, surgical pathology addenda contain information that significantly alters the report and thus merit an amendment. Quality monitoring initiatives that evaluate pathologist and departmental performance should assess both addenda and amendments.
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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 | 0.123 | 0.371 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".