Lactic Acidosis with Chloramphenicol Treatment in a Child with Cystic Fibrosis
Bibliographic record
Abstract
Children with cystic fibrosis are commonly colonized with multi-resistant bacteria. In such patients, infectious exacerbation may require salvage therapy with uncommonly used antimicrobials, including chloramphenicol. Chloramphenicol is rarely used nowadays because of the associated severe adverse events. We describe the case of a 15-year-old female with terminal cystic fibrosis who required intravenous (IV) chloramphenicol treatment for a Burkholderia cepacia (B. cepacia) exacerbation. The child subsequently developed lactic acidosis and secondary respiratory compensation adding to her baseline respiratory distress. Based on the Naranjo scale, the probability of chloramphenicol being the cause of the hyperlactatemia and associated respiratory distress was rated as probable, as the adverse effects resolved upon discontinuation of the drug. Subsequent genotyping for mitochondrial polymorphism (G3010A) confirmed a possible susceptibility to lactic acidosis from mitochondrial RNA-inhibiting agents such as chloramphenicol. Hyperlactatemia is a rare but life threatening adverse effect that has been previously reported with chloramphenicol exposure, but is not generally thought of. Clinicians should be aware of this potentially life threatening, but reversible adverse event. Lactate should be monitored under chloramphenicol and it should be discontinued as soon as this complication is suspected, especially in patients with low respiratory reserve.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".