Noninvasive assessment of varices needing treatment in patients with advanced chronic liver disease: No one should be left behind
Bibliographic record
Abstract
Potential conflict of interest: Nothing to report. TO THE EDITOR: We read with interest the article by Abraldes et al. reporting on several prediction methods to assess noninvasively the presence of esophageal varices needing treatment (VNT) that were retrospectively tested in various cohorts of patients with compensated advanced chronic liver disease in Europe and Canada.1 The authors observed that in patients with VNT, the platelet/spleen ratio (PSR) had a diagnostic accuracy (0.74; 0.67‐0.79) that was not different from the accuracy of the ratio between transient elastography value and PSR (liver stiffness to spleen/platelet score [LSPS]) (Area under the curve [AUC] 0.79; 0.72‐0.84, 95% confidence interval [95%CI]) and was better than liver stiffness measurement alone (AUC = 0.67; 0.59‐0.72, 95% CI), suggesting that both PSR and LSPS could be used to identify a subset of patients with compensated advanced chronic liver disease where endoscopy could be safely avoided.2 We feel that the results reported in this study are relevant for the management of patients and decisions regarding the most appropriate allocation of limited resources, such as endoscopy. However, we also feel that when assessing the accuracy of a diagnostic test, all patients who could benefit from application of the test should be included in the analysis. Transient elastography, which is part of the LSPS as well as of all the other noninvasive tests considered in this study (except the PSR), is a diagnostic test with a nonnegligible rate of unreliable measurements or failures.3 In particular, a French study including 13,369 examinations revealed a 3.1% failure rate and 15.8% unreliability rate for liver stiffness measurements, figures confirmed by a Chinese study that recorded failure and unreliability rates of 2.7% and 11.6%, respectively.3 Therefore, we suggest that the results reported by Abraldes et al. should be corrected in an “intention‐to‐test” analysis in which unreliable measurements and failure of transient elastography are included in the analyses and are considered failures of the diagnostic test, given that these patients are recommended for endoscopy from a practical perspective. On the contrary, spleen bipolar diameter can always be determined and is highly reproducible in patients with compensated cirrhosis.5
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.006 |
| 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".