Bibliographic record
Abstract
In Reply: We thank Dr Al-Najar and colleagues for their letter.1 Our research letter2 from work completed early April 2020 is intended to rapidly communicate our finding of 93% 28-day survival (95% CI = 92% to 95%) for a normal D-dimer on admission and 98% survival (95% CI = 96% to 99%) for a normal D-dimer 2 days later. Because we reported on 749 patients, those estimates had high precision. We chose death as a hard relevant outcome, rather than severity. A total of 100% of patients had criterion standard PCR RNA positivity. D-dimers are most useful when normal, as with venous thromboembolism evaluation. McGIll's Emergency Department may not see the onslaught of COVID-19 suspects coupled with delayed and limited COVID-19 PCR result availability that plagues so many other hospitals and patients. We suggested that an elevated D-dimer could aid in diagnosing serious COVID-19 for the latter. Regarding reporting outcomes other than death and D-dimer, another manuscript has been under review elsewhere. Regarding a 2 × 2 table for PCR-positive patients, the data are already in our research letter but require facility with subtraction: with a normal D-dimer in 586 of 671 survivors, subtraction shows that it was elevated in 85, and with an elevated D-dimer in 36 of 78 nonsurvivors, subtraction shows that it was normal in 42. The Day 3 results are similarly already presented. Our findings are for triage. They are not the last word. Replicating findings is always important but wrenching numbers of unattended deaths and morbidity and mortality of our fellow health care workers led us to urgently submit these transparent data and suggest how to use them. We disagree that doing so is "far too bold."
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.036 | 0.033 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".