September 2019 at a Glance: Focus on Devices
Bibliographic record
Abstract
Inotropic therapy: will failures stop?Inotropic agents did not improve outcomes in patients with heart failure (HF). 1 Ahmad et al. 2 summarize the results with inotropic agents in acute and chronic HF and discuss the main causes of failure and, importantly, give directions for future clinical studies. Devices Controlled decongestionSafe and effective treatment of congestion remains a major unmet need.3,4 Biegus et al. 5 investigated the efficacy of adding to a standard diuretic-based regimen a device which continuously monitors urine output and delivers a matched volume of hydration fluid sufficient to maintain the set fluid balance rate.Patients with acute HF underwent 24 h of standard diuretic therapy followed by 24 h of diuretics with this device.The primary efficacy endpoint of actual fluid loss not exceeding the target fluid loss at the end of therapy was met in all patients.Diuresis was larger, patients' symptoms improved, central venous pressure decreased, and serum creatinine dropped with the new device.5
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.327 | 0.203 |
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".