Addressing the Diagnostic Miscommunication in Pathology
Bibliographic record
Abstract
OBJECTIVES: The pathology report serves as a crucial communication tool among a number of stakeholders. It can sometimes be challenging to understand. A communication barrier exists among pathologists, other clinicians, and patients when interpreting the pathology report, leaving both clinicians and patients less empowered when making treatment decisions. Miscommunication can lead to delays in treatment or other costly medical interventions. METHODS: In this review, we highlight miscommunication in pathology reporting and provide potential solutions to improve communication. RESULTS: Up to one-third of clinicians do not always understand pathology reports. Several causes of report misinterpretation include the use of pathology-specific jargon, different versions of staging or grading systems, and expressions indicative of uncertainty in the pathologist's report. Active communication has proven to be crucial between the clinician and the pathologist to clarify different aspects of the pathology report. Direct communication between pathologists and patients is evolving, with promising success in proof-of-principle studies. Special attention needs to be paid to avoiding inaccuracy while trying to simplify the pathology report. CONCLUSIONS: There is a need for active and adequate communication among pathologists, other clinicians, and patients. Clarity and consistency in reporting, quantifying the level of confidence in diagnosis, and avoiding misnomers are key steps toward improving communications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".