Corrigendum to “Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with biliary tract cancer”
Bibliographic record
Abstract
The authors report that in the original publication the ESMO-MCBS v1.1 scores for durvalumab-cisplatin-gemcitabine and pembrolizumab-cisplatin-gemcitabine are 4 and 1, respectively.The higher ESMO-MCBS v1.1 score for cisplatin-gemcitabinedurvalumab is due to the high 2-year OS gain observed with this regimen (14.2%).This was, however, based on only 9 patients (2.6% of the durvalumab-treated patients) who were still alive at that time.Thus, it should be noted that it does not currently provide evidence that durvalumab is vastly superior to pembrolizumab in combination with cisplatin-gemcitabine as both drugs incur similar clinical benefit with a HR OS of 0.75 and 0.83 and absolute median OS benefit of 1.6 and 1.8 months, respectively.Future updates to the ESMO-MCBS methodology will account for the proportion of study patients included in tail of the curve survival analyses, resulting in a lower MCBS score for durvalumab-cisplatin-gemcitabine.Recommendation 4a should read as follows: "Recommendation 4a.The combination of cisplatin-gemcitabine with durvalumab or pembrolizumab should be considered as standard of care in first-line BTC [I, A; ESMO-Magnitude of Clinical Benefit (MCBS) v1.1 score for durvalumab: 4; ESMO-MCBS v1.1 score for pembrolizumab: 1].Cisplatin-gemcitabine-S1 is an alternative therapeutic option for fit patients [II, B]."The revised Figure 1 Algorithm for the treatment of biliary tract cancer is given below.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.111 | 0.103 |
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".