Creating an (ethical) epistemic space for the normalization of clinical and “real food” oral immunotherapy for food allergy
Bibliographic record
Abstract
Researchers and sociologists have argued the consequences of standardization vis-à-vis clinical practice guidelines are diverse and argue they should be explored empirically. Sociologists have also argued that "best evidence" for the development of clinical practice guidelines is not restricted to randomized controlled trials and that other forms of knowledge should be embedded in and inform CPGs. There is little research concerning how other types of knowledge are mobilized and taken up in CPGs. This article presents the results of an ethnographic investigation in Canada between 2015 and 2020 of the development of a clinical practice guideline for immunotherapy for food allergy. My research shows that immunotherapy has become the source of controversy regarding whether immunotherapy should be offered in the clinic or remain experimental and whether it should be offered using food or commercial products. I argue that the clinical practice guideline for oral immunotherapy reaffirms what has been previously noted by sociologists; guidelines can serve normative purposes and are not merely technical documents. This case study is unique as it demonstrates how guidelines can serve as "community-making devices" to consolidate "epistemic communities" through the explicit and formal mobilization of ethical principles alongside other forms of "traditional" evidence. The mobilization of a multi-criteria approach that included ethical principles was mobilized in part to counter the de-legitimization and peripheralization of clinical and real food oral immunotherapy.
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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 | 0.266 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.026 | 0.151 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".