Intraoperative Communications Between Pathologists and Surgeons: Do We Understand Each Other?
Bibliographic record
Abstract
CONTEXT.—: Clear communication between pathologists and surgeons during intraoperative consultations is critical for optimal patient care. OBJECTIVE.—: To examine the concordance of intraoperative diagnoses recorded in pathology reports to surgeon-dictated operative notes and assess the impact of an intervention on the discrepancy rates. DESIGN.—: Discrepancies between the intended communication by pathologists and the interpretation by surgeons were characterized as minor with no crucial clinical impact, and major with the potential of altering patient management. After analysis, a corrective intervention was implemented with education, information sharing, and a change in protocol, and a comparative analysis was conducted. RESULTS.—: We examined 223 surgical cases with 578 intraoperative consultations. In 23% (51) of the cases, the intraoperative diagnosis was not recorded in the operative reports. We found minor discrepancies in 34% (59) and major discrepancies in 2% (3) of the remaining cases. Deferrals accounted for 24% (14 of 59) of the minor and 33% (1 of 3) of the major discrepancies. Among the discrepant cases, 56% (35 of 62) were multipart cases, including all major discrepancies. Following intervention, no major discrepancies were found in 101 cases with 186 intraoperative interpretations. The cases with no operative documentation reports decreased from 23% to 16% (16 of 101). Minor discrepancies were found in 11% (9 of 85) of the cases, indicating significant improvement (P < .001). CONCLUSIONS.—: Intraoperative diagnoses can be miscommunicated and/or misinterpreted, possibly impacting intraoperative management, particularly in multipart cases and those involving deferrals. This study highlights the importance of auditing intraoperative communications and addressing the findings through a local intervention.
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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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".