Interinstitutional Pathology Consultations: A Reassessment
Bibliographic record
Abstract
We retrospectively determined the clinical impact of 1,000 randomly selected interinstitutional pathology consultations (IPCs). An IPC included all specimens from the patient. IPCs were classified as concordant or discordant with the original diagnosis. Discordant IPCs were classified as having a clinical impact or no impact. Discordant IPCs owing to interpretation differences were subclassified further. The IPCs included 1,522 specimens (1,204 histology, 318 cytology); 923 (92.3%) were concordant, 9 (0.9%) indeterminate, and 68 (6.8%) discordant (clinical impact, 37; no impact, 31). Reasons for discordant IPCs were interpretation differences, 45; additional sectioning, 7; ancillary testing, 1; clerical error, 5; or a combination, 10. Reasons for 26 discordant IPCs with clinical impact owing to interpretation differences were overdiagnosis, 11; tumor subtype change, 4; stage change, 4; underdiagnosis, 3; resection margin status change, 2; undergrading, 1; and understaging with resection margin status change, 1. IPC may identify diagnostic discrepancies that impact management for some patients. The prevalence of a clinical impact of IPC on management varies according to body site. Mandatory IPC does ensure identification of clinically significant diagnostic discrepancies; targeted IPC by body site or specimen type may represent an alternative strategy after further data accumulation. Discordant IPCs may be due to factors other than interpretation difference.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".