Pseudo-outbreak of haemodiafiltration dialysis fluid contamination: results of a detailed epidemiologic investigation
Bibliographic record
Abstract
Background: Compliance with dialysis fluid ultrapurity standards is a paramount for online modalities. More than 200 dialysis fluid samples have been analyzed monthly for years in our two dialysis units, with compliant microbiological results until mid-2020. Aim: In mid-2020, an unusual occurrence (30%) of contaminated dialysis fluids in dialysis units led us to investigate to determine the source. Methods: Microbiological methods for aquaphilic bacteria culturing and endotoxin detection in dialysis fluids were routinely performed on a monthly basis for all dialysis machines. As the contamination appeared randomly and almost simultaneously in our two units without any routine change or febrile syndrome, we searched for a common cause. Supplier's sampling kits as well as microbiological laboratory procedures were scrupulously investigated. Findings: 21 out of 30 sampling bags filled with sterile water brought back numerous fungi and bacteria. Laboratory's investigation, through the negative control tests performed routinely, exonerated the lab. All batches of bags analyzed later showed variable levels of contamination according to their transport/storage mode or date of manufacturing. Analyses performed by the supplier - methods complying with the medical device's standards but different from those recommended for dialysis fluids purity - remained negative. Conclusion: Our investigation revealed that the contamination of our sampling kits came presumably from the manufacturer's supplying chain. Such false-positive results findings, created serious safety issues and disturbed clinical activities since positive machines were quarantined. Furthermore, it raised a serious concern about manufacturing, microbiological checking and shipping methods for the medical device industry that deserve further attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".