Bibliographic record
Abstract
The highest level of support for evidence based decisions is the randomized controlled trial (RCT); however, RCT results are only useful if the study has strong internal and external validity. There have been a number of clinical trials that have addressed the issue of the optimal platelet dose; however, none of these studies have provided definitive data on the optimal platelet dose due to a variety of methodological issues associated with the study designs. Currently two randomized controlled trials have been implemented to address the issue of optimal platelet dose. The results of these trials will not be available until 2007–2008. The BEST (Biomedical Excellence for Safer Transfusion) Collaborative has initiated a platelet dose study comparing the frequency of WHO bleeding Grade 2 with low and standard dose platelets. The Transfusion Medicine/ Haemostasis Clinical Trials Network (CTN) is also performing a platelet dose study comparing three treatment strategies (high, standard and low dose platelets). There were numerous methodological issues that had to be considered when designing these two studies. More recently some European investigators have questioned the need for prophylactic platelet transfusions and several studies are currently underway to investigate the efficacy of changing this practice.
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 | 0.150 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".