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Record W2106615816 · doi:10.1086/432625

Efficacy of Echinilin for the Common Cold

2005· letter· en· W2106615816 on OpenAlexafffund
R. Barton

Bibliographic record

VenueClinical Infectious Diseases · 2005
Typeletter
Languageen
FieldMedicine
TopicHerbal Medicine Research Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsMedicineCommon coldImmunology

Abstract

fetched live from OpenAlex

Sir—As coinvestigator in a clinical study of echinacea [1] discussed in the recent article by Caruso and Gwaltney [2], I would like to point out numerous errors in their characterization of our study as “not well designed.” They claim that our study lacked proper randomization, intention-to-treat analysis, proof of blinding, and validated case definition. Our study was misrepresented in all of those points. With respect to random assignment, our study states that “subjects were randomly assigned to receive either the Echinacea or placebo” [1, p. 77]. The method we used is the same as that used in the study by Barrett et al. [3], which Caruso and Gwaltney [2] concluded met their criteria for randomized assignment. With respect to intention-to-treat analysis, again, the authors failed to note that our results, which were based on intention-to-treat, were published alongside the per protocol results. Every figure in our paper contained intention-to-treat analysis as well as the per protocol analysis. The discussion of both results is included. With respect to blinding, our study states that “blinding was also maintained adequately during the treatment period. On completion of the study, ∼50% of the subjects in both groups could not guess correctly whether they had received echinacea or placebo” [1, p. 78]. Our assessment of blinding is almost identical to that made in the study by Taylor et al. [4], which Caruso and Gwaltney [2] considered to have met their criteria for proof of blinding. With respect to the validated case definition, once again, Caruso and Gwaltney [2] failed to note that the symptom scale used in our study is the same 10-point Likert scale used in Barrett et al. [3]. In our study, the subjects were enrolled at onset of the first symptom, whereas in the study by Barrett et al. [3], study subjects were enrolled after the onset of 2 symptoms. It should be noted that the randomization process would have corrected any results due to this difference. In our study, both the echinacea group and the placebo group would have started their treatment at the onset of the same number of symptoms, and there is no difference in case definition between the 2 groups. Finally, Caruso and Gwaltney [2] failed to address the most fundamental deficiency in the 2 “perfect” studies (Barrett et al. [3] and Taylor et al. [4]), as well as the other articles they chose to include in their meta-analysis. None of these studies used a standardized echinacea extract. In contrast, the echinacea preparation used in our study was standardized and had already been shown to be effective in animal studies [5, 6]. We are disappointed in Caruso and Gwaltney's superficial review of our paper and the misrepresentation that resulted. We sincerely hope that these errors will be rectified in your journal at the earliest opportunity. Potential conflicts of interest. R.B. has provided consultation services to Factors R & D Technologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.023
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0230.011
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.090
GPT teacher head0.462
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2005
Admission routes2
Has abstractno

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