Corticosteroids for Caustic Esophageal Burns
Bibliographic record
Abstract
To the Editor: Although we read with interest the recent meta-analysis on the use of corticosteroids for the prevention of strictures following caustic esophageal injuries (1), we question the accuracy of results. After the ground-breaking efforts of Rosenberg et al (2,3), researchers attempted to define which patients with caustic ingestions would benefit from corticosteroids. The 3 included studies use 3 different regimens (dose and duration) of 2 different corticosteroid preparations. It is invalid to assume that all regimens from a given drug class are therapeutically equivalent without comparative data. In addition, 2 of the study's results are diluted by the inclusion of grade 1 injuries that almost never progress to strictures and grade 3 injuries that inevitably either perforate or progress to strictures (4,5). Finally, 1 study combines data for acid and alkali injuries (5). This degree of heterogeneity invalidates any attempt to combine these 3 trials as a whole. To date, only 1 study uses a low-risk short-course corticosteroid administration protocol in patients with a high likelihood of stricture progression (grade 2b), and a low likelihood of perforation (6). The findings of this trial cannot and should not be negated by an inadequate meta-analysis. Rather, they should be confirmed or questioned based on replication or by a meta-analysis selecting injuries that are comparable and using similar treatments. Unfortunately, we are not aware of studies with such similarities. Pending new results, we suggest following Usta's protocol as the cost is negligible, the likelihood of harm is minimal, and the potential for benefit seems great.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".