Physician and Surgeon Communication Assessed via the Pathology Requisition in a Regional Laboratory Over Ten Years
Bibliographic record
Abstract
Background Ineffective communication between healthcare providers is a known risk factor for adverse events. Objective The aim of this study was to retrospectively assess the communication with pathology via an analysis of the information provided on the pathology requisitions over ten years. Methods All in-house surgical specimens and all non-gynecologic cytopathology specimens accessioned from 2011 to 2020 were retrieved at a regional laboratory. Cases with any clinical information were deemed to have a clinical history present (CHP). CHP was tabulated by submitting physicians/surgeons (SPS), hospital site, year, and tissue group. Results The study period contained 554,817 relevant pathology reports, of which 553,966 could be extracted. The overall CHP rate was 74% and varied from 76% to 67% over the study period. SPSes submitting ≥200 cases (n=314) had a mean/median/standard deviation/max/min CHP rate of 81%/92%/23%/100%/5%. The CHP varied between four hospital sites, from 53% to 97%. CHP varied from 61% to 99% by tissue group. Conclusions CHP is associated with several factors and appears to depend on the hospital culture, specialty, and individual physician/surgeon. The pathology requisition is a way to measure and track the communication that is clinically relevant. Improving communication with pathologists/the pathology department will likely require process changes and mandates. Hospital and laboratory accreditation bodies should consider effective communication with pathology a marker of quality and an accreditation issue.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".