Interobserver agreement for post mortem renal histopathology and diagnosis of acute tubular necrosis in critically ill patients
Bibliographic record
Abstract
BACKGROUND: The renal histopathology of critically ill patients dying with acute kidney injury (AKI) in intensive care units of high income countries remains uncertain. METHODS: Retrospective observational assessment of interobserver agreement in the reporting of renal post mortem histopathology, and the ability of pathologists blinded to the clinical context to independently identify the presence of pre-mortem AKI from digital images of histological sections from 34 critically ill patients dying in teaching hospitals in Australia and Canada. RESULTS: We identified a heterogeneous cohort with a median age of 65 years (interquartile range [IQR], 56.5-77), APACHE II score of 27 (IQR, 19-33), and sepsis as the most common admission diagnosis (12/34; 35%). The most common proximate causes of death were cardiovascular (19/34; 56%) and respiratory (7/34; 21%) failure. AKI was common, with 23 patients (68%) developing RIFLE-F AKI, and 21 patients (62%) receiving renal replacement therapy. Structured reporting for tubular inflammation showed excellent agreement (kappa = 1), but no other subdomain demonstrated better than moderate agreement (kappa < 0.6). Only fair agreement (55.9% of cases; kappa = 0.23) was demonstrated on the diagnosis of moderate to severe acute tubular necrosis (ATN). Pathologist A predicted RIFLE-I or worse AKI with the diagnosis of ATN, with an overall accuracy of 61.8%; pathologist B predicted AKI with an accuracy of 35.3%. CONCLUSIONS: Post mortem assessment of the renal histopathology in critically ill patients is neither robust nor reproducible; independent pathologists agree poorly on the diagnosis of ATN, and their structural assessment appears dissociated from ante-mortem renal function.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".