Reply
Bibliographic record
Abstract
Potential conflict of interest: Nothing to report. Author names in bold designate shared co‐first authorship. Thank you for the opportunity to reply to the letter by our colleagues, in which an important point is addressed regarding the interpretation of liver stiffness measurements (LSMs), performed by transient elastography, in population‐based studies. The letter is in reference to our article published in Hepatology in January 2016, in which we described our observations among 3,041 presumed healthy individuals who had undergone hepatic ultrasonography and transient elastography measurements.1 In this population, we found a median LSM of 4.7 kPa (interquartile range 3.8‐5.8), and in 5.6% LSM was ≥8.0 kPa. At this 8.0 kPa cutoff, in most studies as well as in clinical practice, liver fibrosis is diagnosed or, at least, suspected. Thiele et al. rightfully state that liver stiffness does not automatically equate with liver fibrosis, especially in populations with low prevalence of chronic liver disease. We fully agree. However, the true prevalence of liver fibrosis in the general population is unknown. This is because liver biopsy, which is the gold standard of liver fibrosis assessment, is not appropriate for use in the general population, considering its invasive nature. This leaves noninvasive tests, such as transient elastography, as the only usable screening options, albeit with limitations. The real question is how healthy this presumed‐healthy population truly is, given the fact that 24% had abnormal liver enzymes (in which 11.3% had LSM ≥8.0 kPa versus 3.7% in those with normal liver enzymes) and 32.8% had ultrasonographic evidence of nonalcoholic fatty liver disease (NAFLD) (in which 8.4% had LSM ≥8.0 kPa versus 3.5% in those without NAFLD). Furthermore, in the only other population‐based study in Caucasians, by Roulot et al.,2 measuring liver stiffness in 1,190 adults attending a medical checkup, 7.5% were found to have LSM ≥8.0 kPa. It is noteworthy that in all cases an underlying liver disease was found, most of them NAFLD. Given the current NAFLD epidemic, maybe the general population is no longer a low‐prevalence population for chronic liver diseases. We therefore agree with Thiele et al. that early detection of chronic liver disease in the general population is of utmost importance and deserves appropriate attention. In fact, this has become the goal of a recently established research network of European centers for investigation of early detection of chronic liver diseases in the general population. This effort is led by Dr. Gines from the Barcelona group and includes several European investigators, including the group by Thiele et al. and the undersigned.3 Hopefully, in the near future, we will learn from this effort and develop better tools for screening. Until then, the existing noninvasive tools, including transient elastography and serological markers, are likely going to be the only screening tools at our disposal in the general population. Interpretation of these findings should always be balanced against the background prevalence, diagnostic performance, and possible confounders.
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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 | 0.003 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.105 | 0.067 |
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".