Reply:
Bibliographic record
Abstract
We thank Dr. Cardoso for his letter about our recent hepatology article validating the easy‐to‐use North American Consortium for the Study of End‐stage Liver Disease (NACSELD) acute‐on‐chronic liver failure (ACLF) score in 2,675 prospectively enrolled patients continent‐wide.1 His main concern was the possibility that patients could meet NACSELD‐ACLF criteria (two or more organ failures) by requiring intubation for airway protection during grade III/IV hepatic encephalopathy.2 This is because our simple bedside tool does not require the calculation of a partial pressure of arterial oxygen (PaO2) to fraction of inspired oxygen (FiO2) ratio like the more complicated Chronic Liver Failure‐Sequential Organ Failure Assessment score.3 In the database we did not collect PaO2 and FiO2 values, so we are unable to calculate this ratio. Therefore, to address this concern, we eliminated all 90 patients who had just these two organ failures and recalculated the statistics (Table 1). Of note, the predictive power of the NACSELD‐ACLF score did not change when we reevaluated only infected patients, only uninfected patients, and all patients together after eliminating these 90 patients. Therefore, the NACSELD‐ACLF score not only stood up to the test of validation in two separate cohorts1 but retained its predictive power in both infected and uninfected patients and remained valid even when 90 ACLF patients were removed and all of the statistics were recalculated. Table 1 - Original and Multivariable Models Predicting 30‐Day Survival Original Revised, Excluding ACLF Resulting from Respiratory and Brain Failure Only Effect Estimate SE χ2 P Estimate SE χ2 P Infected patients NACSELD‐ACLF –1.8435 0.2297 64.39 <0.0001 –1.9670 0.2629 55.99 <0.0001 MELD –0.0508 0.0140 13.14 0.0003 –0.0514 0.0146 11.83 0.0006 WBC –0.6561 0.1282 26.17 <0.0001 –0.6353 0.1298 23.95 <0.0001 Albumin 0.2168 0.1561 1.93 0.16 0.2291 0.1635 1.96 0.16 Uninfected patients NACSELD‐ACLF –1.2258 0.3281 13.96 0.0002 –0.9130 0.4028 5.14 0.02 MELD –0.0971 0.0146 44.09 <0.0001 –0.1000 0.0150 44.64 <0.0001 WBC –0.4146 0.1181 12.33 0.0004 –0.3790 0.1230 9.50 0.002 Albumin 0.2707 0.1761 2.36 0.12 0.2830 0.1830 2.39 0.12 All patients NACSELD‐ACLF –1.7390 0.1890 84.62 <0.0001 –1.7098 0.2176 61.74 <0.0001 Age –0.0475 0.0082 33.27 <0.0001 –0.0472 0.0086 30.16 <0.0001 WBC –0.5547 0.0830 44.65 <0.0001 –0.5301 0.0845 39.34 <0.0001 Albumin 0.3055 0.1179 6.71 0.01 0.3331 0.1230 7.34 0.007 MELD –0.0852 0.0106 64.95 <0.0001 –0.0862 0.0110 61.43 <0.0001 Had infection –0.4015 0.1660 5.85 0.02 –0.4129 0.1726 5.72 0.02 Abbreviations: MELD, Model for End‐Stage Liver Disease; WBC, white blood cell count. Dr. Cardoso’s second concern was with our proposal that the NACSELD‐ACLF score may help to determine the futility of continued aggressive care in hospitalized patients with cirrhosis. To be clear, we are not advocating the use of the NACSELD‐ACLF score in isolation to determine futility. Instead, we feel the NACSELD‐ACLF score is one essential, simple bedside tool for clinicians to use when evaluating a patient’s prognosis. Many, if not most, patients with four‐organ system failure will not derive benefit from further aggressive intensive care; however, other factors such as age, transplant candidacy, patient/family preferences, and early clinical improvement play key roles in making the final decision of whether or not to pursue further aggressive care versus comfort care in an individual patient.4 It is also important to consider that although a patient may survive an inpatient stay after developing NACSELD‐ACLF, this does not tell us the longer‐term prognosis, risk for readmission, or quality of life.5 Potential conflict of interest Nothing to report.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.035 |
| Insufficient payload (model declined to judge) | 0.023 | 0.022 |
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".