Synoptic Reporting in Clinical Placental Pathology: A Preliminary Investigation Into Report Findings and Interobserver Agreement
Bibliographic record
Abstract
INTRODUCTION: Placental pathology is key for investigating adverse pregnancy outcomes, however, lack of standardization in reporting has limited clinical utility. We evaluated a novel placental pathology synoptic report, comparing its robustness to narrative reports, and assessed interobserver agreement. METHODS: 100 singleton placentas were included. Histology slides were examined by 2 senior perinatal pathologists and 2 pathology residents using a synoptic report (32 lesions). Historical narrative reports were compared to synoptic reports. Kappa scores were calculated for interobserver agreement between senior, resident, and senior vs resident pathologists. RESULTS: Synoptic reporting detected 169 (51.4%) lesion instances initially not included in historical reports. Amongst senior pathologists, 64% of all lesions examined demonstrated fair-to-excellent agreement (Kappa ≥0.41), with only 26% of Kappas ≥0.41 amongst those examined by resident pathologists. Well-characterized lesions (e.g., chorioamnionitis) demonstrated higher agreement, with lower agreement for uncommon lesions and those previously shown to have poor consensus. DISCUSSION: Synoptic reporting is one proposed method to address issues in placenta pathology reporting. The synoptic report generally identifies more lesions compared to the narrative report, however clinical significance remains unclear. Interobserver agreement is likely related to differential in experience. Further efforts to improve overall standardization of placenta pathology reporting are needed.
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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 | 0.194 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".