Why A-level evidence does not make it to clinicians' A-list: the case of thromboprophylaxis in medical patients
Bibliographic record
Abstract
> A woman in her 40s is admitted to hospital for worsening scleroderma that involves the gastrointestinal tract, skin, and lungs. Although she shows some clinical improvement, 7 days into her hospitalisation a code blue is called after she is found to be unresponsive. Resuscitation attempts are unsuccessful. The presumed cause of death is acute pulmonary embolism. A review of her health record indicates that she was not receiving an intervention to prevent deep venous thrombosis (DVT). Could DVT prophylaxis have saved this patient’s life? Each year, more than 6 people in every 1000 will develop DVT, and 1 will die from pulmonary embolism (PE)1—more deaths than from breast cancer, AIDS, or motor vehicle accidents. Unlike the latter conditions, DVT is relatively easy to prevent and treat, but in far too many cases, measures to prevent DVT and its embolic sequelae are overlooked. In contemporary audits of DVT prophylaxis practices, 65–83% of hospitalised medical patients at risk of DVT were not receiving prophylaxis.2 3 What is difficult to reconcile is that such practices occur in the face of strong A-level evidence that anticoagulants should be considered in all at-risk medical patients.4 Indeed, …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.066 | 0.356 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.026 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".