Assessing clinical and psychological features: who are patients showing a nocebo re-action during the drug challenge test?
Bibliographic record
Abstract
The nocebo reaction, namely the undesirable effect of an inert substance (placebo), is a phenomenon rarely investigated in literature.A better knowledge of this reaction may help clinicians in the management of these patients in clinical practice.Patients with drug adverse reactions (ADR) undergoing the drug challenge test are an ideal model for studying the nocebo effect, and the study aims to investigate their clinical and psychological features.One hundred and twenty patients (M age = 46.59,SD = 15.5;82% female), of which 90 non responders and 30 with nocebo reactions (25%) were recruited, and completed a battery of psychological measures: State-Trait Anxiety Inventory X1-X2, Beck Depression Inventory II, Symptoms Checklist-90-R, Difficulties in Emotion Regulation Scale, Toronto Alexithymia Scale.Clinical features (individual characteristics and ADR clinical history) were collected by clinicians.The results show that older age (p = 0.002), low level of education (p = 0.039) and a depressive tendency (p = 0.030) appear to be potential risk factors for nocebo effects.Although none of the features related to the previous clinical history appear to represent a risk factor for the nocebo reactions (p > 0.05), significant correlations between some of the clinical and psychological characteristics considered (p values from 0.005 to 0.042) help to better delineate the profile of these reactive patients.A specific training of the sanitary team about psychological aspects is recommendable.Patients with adverse drug reactions (ADR) are an ideal model for studying the nocebo effect, because their previous experience can generate a negative expectation conditioning their acceptance and results of subsequent therapies.In many ADR cases, the allergy diagnostic workup includes the systemic challenge (oral or parenteral), to confirm the responsibility of a drug in the reaction and to identify alternative drugs that can be safely used (6).The experience of allergists is that some patients may show negative reactions to the administration of an inert substance (placebo) which usually precedes the active drug.The practice using placebo has the purpose to better evaluate the test results, evidencing a possible adverse reaction -the nocebo effect -that is reported by literature in percentages ranging from 3% to 27% (7,8,9).Lombardi and colleagues (8) stressed that the
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".