Making the most of your pathology: standardized histopathology reporting in head and neck cancer.
Bibliographic record
Abstract
OBJECTIVES: Inconsistencies in pathology reporting can contribute to treatment delays and, potentially, inadequate or inappropriate postoperative therapy for patients with malignant disease. Given their importance, there is growing interest in optimizing the reproducibility and readability of pathology reports. The purpose of this study was twofold: (1) to assess the quality and completeness of current head and neck pathology reports in the Calgary Health Region and (2) to examine the effects of a standardized pathology report on clinician comprehension and proposed patient management. METHODS: A retrospective review examining the quality and completeness of current head and neck pathology reports was conducted. This was followed by a prospective survey of Canadian head and neck surgeons. Participants were asked to read a traditional freeform pathology report and a standardized pathology report and then complete a brief questionnaire. Comparisons between the responses were then made. RESULTS: Our retrospective analysis demonstrated considerable variation in the completeness of current freeform head and neck pathology reports. The results from our prospective survey establish that our standardized pathology report required significantly less time to read and was preferred by the majority of respondents. In addition, comprehension tended to be higher after reading the standardized pathology report. CONCLUSION: Standardized pathology reports are known to enhance report quality and consistency. We demonstrate in this study that they require less time to read, are better received, and do not negatively impact reading comprehension, potentially making them an effective and feasible alternative to traditional, freeform pathology reports.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".