Thiamine deficiency in congestive heart failure patients receiving long term furosemide therapy.
Bibliographic record
Abstract
OBJECTIVE: To assess the presence of thiamine deficiency in congestive heart failure patients receiving furosemide therapy. DESIGN: Prospective, biochemical analysis of thiamine status was performed in outpatients and inpatients of the University of Ottawa Heart Institute. SUBJECTS: Thirty-two patients with congestive heart failure who received at least 40 mg/day of furosemide were included. Patients were then separated into two groups depending on whether the dose of furosemide was greater than or equal to 80 mg/day. METHODS: The primary measure was actual thiamine status as assessed by the erythrocyte transketolase enzyme activity and the degree of thiamine pyrophosphate effect. RESULTS: Biochemical evidence of severe thiamine deficiency was found in 98% (24 of 25) patients receiving at least 80 mg/day of furosemide and in 57% (four of seven) of patients taking 40 mg furosemide daily, odds ratio (OR) 19.0 (1.13<OR<601.29). Thiamine status was not associated with any other clinical variables. CONCLUSIONS: These findings suggest that thiamine deficiency occurs in a substantial proportion of congestive heart failure patients being treated with furosemide.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".