Bibliographic record
Abstract
Trisodium citrate 33% as anticoagulant is routinely used in several indications for acute dialysis patients, with very good clinical tolerance and results. But in chronic dialysis patients two problems arose after some weeks : 1/ In all of them hypernatremia and alcalosis developed : the pre dialysis sodium and bicarbonate level raised progressively. This could be tempered by lowering the sodium and dialysate bicarbonate concentration respectively from 140 mEq/l to 132 mEq/l and from 37 mEq/l to 25 mEq/l. 2/ The second problem was a progressive rise in serum Aluminum (Al) level in some patients: from a mean 3 μg/l to 38 μg/l; after excluding water, concentrate, dialysate, medication, etc. as possible cause, we finally controlled the Al level in the glass bottles, containing the trisodium citrate solution, and we noted very high values:35,300 μg/l. After replacement of the glass bottles by polyvinyl bags (with a neglectable Al content), the serum Al levels returned to normal. It is known that citrate chelates also the Al present in the glass of the bottles or vials themselves; our warning is not to use glass containers for citrate solutions. A short overview of different glass types is given.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".