Bibliographic record
Abstract
Sir, We appreciate the thoughtful comments by Dr Pintavorn and the opportunity to provide some more information on the vexing and little known issue of drug-induced phospholipidosis. First, Dr Pintavorn points out that amiodarone, which we listed as another drug that is capable of causing phospholipidosis, has never been proven to result in renal deposits in humans. A number of reports have reported histology-proven renal phospholipidosis in animal models. Mortuza and co-workers observed lipid accumulation in the lung and liver of amiodarone-treated rats [ 1 ]. Baronas and colleagues showed accumulation of inclusions in the kidney of rats treated with amiodarone [ 2 ]. Many patients on amiodarone receive anticoagulation with aspirin if not warfarin. There is often a compelling reason for such anticoagulation, such as a coronary stent or cardiac valve replacement. Many nephrologists are reluctant to proceed to biopsy in this scenario and rightly so. It is therefore, we believe, conceivable that cases of mild renal phospholipidosis are lurking among many patients with cardiac disease and renal impairment in whom vascular disease is assumed. Similar surprises have been encountered in renal biopsies from lung transplant recipients in whom nephrologists are equally reluctant to perform renal biopsies [ 3,4 ].
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.037 | 0.036 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".