Bibliographic record
Abstract
Stephen M Perle, DC, MS* Eisenberg et al.1 have shown what I believe to be their bias in their paper detailing the credentialing of complementary and alternative medical providers (CAM). Eisenberg and colleagues (3 of whom are MDs) deal with this topic in a way that I believe is consistent with their status as the majority health care providers. The biases or arrogance with which the majority deals with a minority is often completely transparent to the members of the majority. Even if they are trying to be dispassionate and unbiased, the bias can be glaring to the minority. The most blatant bias in Eisenberg’s1 paper would probably be obvious to any of the regular readers of this journal. It is easy to say that it is an issue that I would have been aware of given my past residence in Canada and my research collaboration with two Canadians.2,3 But suffice it to say that at the least my awareness of Eisenberg’s bias is heightened by whom I am writing this commentary for, a Canadian chiropractic journal. It must be apparent now that the bias I am referring to is that their paper covers credentialing of CAM providers in the United States only, but does not say this anywhere in the abstract or paper. I suppose one could assume that it is a given that the paper is about the credentialing of CAM providers in the States because it appears in the official journal of the American College of Physicians and American Society of Internal Medicine. However a Medline search of Annals of Internal Medicine showed that of 18864 articles, 134 are by Canadians or about Canada is some way. So this journal while rarely publishing papers by or about Canadians does do so rarely. In the first paragraph Eisenberg et al.’s bias or should I said their medical superiority complex shines through clearly. They say, “Legislative recognition trumps medical recognition: State legislature can license providers and thereby grant citizens access to certain therapies, even if scientific debate has not concluded in favor of those modalities.” To me this is the most troubling statement in the whole paper for the implication here is two fold: Having biases seems to be a fundamental human trait. We have biases about almost everything. It is a constant battle to try to control our biases. In science one often goes to extreme measures to remove the bias of subjects and researchers. This is done with blinding and control. The purpose of the experimental design is to as effectively as possible remove bias from the study. In some studies one cannot systematically avoid bias but one must be aware of the possibility of bias affecting a study or one’s writing and be vigilant for its appearance.
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 | 0.015 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".