On-Treatment Change in <scp>d</scp> -Dimer Is Associated With Differential Outcomes Among Therapeutic Dose Heparin-Treated Noncritically Ill Patients Hospitalized for COVID-19
Bibliographic record
Abstract
This study is reporting on a clinical randomized control trial: Yes Clinical trial reports should comply with the Consolidated Standards of Reporting Trials ( CONSORT) including its additional extensions as appropriate.This should include a flow diagram presenting the screening, enrollment, intervention allocation, follow-up, and data analysis with number of subjects for each.Please refer specifically to the CONSORT Checklist of items to include when reporting a randomized clinical trial and provide the completed checklist at the time of submission.Please confirm and provide the following details at this time: Clinical Trial RegistrationClinical trials should be registered in an appropriate online trial registry at or before the onset of participant enrollment.In the box below, please provide the name of the trial registry, the registry URL, and the trial registration number.If your study is a clinical trial but not registered, please provide an explanation below.https://classic.clinicaltrials.gov/ct2/show/NCT04505774 Sex and Race/Ethnicity Specific ResultsSex and race/ethnicity specific results of the trial's primary outcomes are reported regardless of whether there are significant differences by sex or race. Yes Diverse Representation -ParticipantsWere steps taken to ensure diverse representation among trial participants, and if so, are these efforts outlined in the methods?If No, a lack of diversity should be explained and listed as a limitation. Yes Diverse Representation -Steering CommitteeWere steps taken to ensure diverse representation among the trial steering committee, and if so, are these efforts outlined in the Methods including demographic information?If No, a lack of diversity should be explained and listed as a limitation.Yes
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 | 0.015 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".