Bibliographic record
Abstract
Amphotericin B is the treatment of choice for severe systemic fungal infections. Nephrotoxicity is the most clinically significant adverse effect, but studies examining nephrotoxicity in children are scarce. Nephrotoxicity includes decreased glomerular filtration rate and distal tubulopathy with urinary loss of potassium and magnesium, renal tubular acidosis, loss of urine concentrating ability, and sometimes Fanconi's syndrome. The mechanisms involved in nephrotoxicity include the use of deoxycholate, the vehicle for amphotericin, reduction in renal blood flow and glomerular filtration rate, increased salt concentrations at the macula densa, interaction of amphotericin with ergosterol in the cell membrane, and apoptosis in proximal tubular cells and medullary interstitial cells. Some risk factors for amphotericin nephrotoxicity have been determined over the years. Cumulative dosage, treatment duration, and dosing schedule as well as the combination of amphotericin with other nephrotoxic drugs, such as diuretics and cyclosporine, are important risk factors. Mechanisms to prevent nephrotoxicity include the use of lipid formulations such as amphotericin B lipid complex, amphotericin B colloidal dispersion, and liposomal amphotericin B and the concurrent use of volume repletion. Amiloride can be considered in serious potassium loss.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".