Unjustified assertions regarding race and ethnicity in clinical decision-making
Bibliographic record
Abstract
unjani et al describe the "effect" of race/ethnicity on semen and hormonal parameters in relation to clinical decision-making. 1 The word "effect" properly describes a causal relation, but race/ethnicity cannot serve as a causal exposure because it cannot be assigned in trials and, thus, is inherently inseparable from ancestry, history, culture, and a myriad of other confounding factors that defy distinct attribution.2 Race/ethnicity may potentially be used predictively or descriptively, but this usage must be justified substantively and statistically, and the authors provide no such justification.The authors repeat the claim four times that the racial differences reported are important for patient management but never how they propose to use this information.It is wellestablished in clinical epidemiology that a risk marker must have a very strong association with the outcome to be used in medical decision-making.An odds ratio (OR) of at least 30 is needed, otherwise most patients will be misclassified.3 For example, the authors report an OR 1.70 for the prediction of azoospermia by noting that a patient is black.Based on calculations in Pepe et al, 3 if black race were to mislabel only 10% of non-azoospermic men as azoospermic, then black race would correctly identify only 16% of all the truly azoospermic men, and 84% percent of the true cases must be missed.On the other hand, if one wants the sensitivity of black race to be higher, so that it captures 80% of true cases of azoospermia, then with OR 1.70 it must necessarily misclassify 70% of the other men as azoospermic.In short, with this magnitude of association, one cannot avoid misclassifying the majority of men by taking black race as a marker for azoospermia.An OR 1.70 is clinically useless, and most of the associations reported are even weaker.Worse, this is a highly selected population with no prospects for internal or external validity.Participants are not only Canadian men with subfertility, but only those willing and able to access clinical intervention for this condition.Then, among the 9079 patients registered in the clinic, over
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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 | 0.095 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.103 | 0.089 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".