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Record W2103401881 · doi:10.1177/0009922806295281

The Attitude of Physicians Toward Cold Remedies for Upper Respiratory Infection in Infants and Children: A Questionnaire Survey

2006· article· en· W2103401881 on OpenAlexaffabout
Raanan Cohen‐Kerem, Savithiri Ratnapalan, Josephine Djulus, Xu Duan, Rahul V. Chandra, Shinya Ito

Bibliographic record

VenueClinical Pediatrics · 2006
Typearticle
Languageen
FieldMedicine
TopicRespiratory and Cough-Related Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineCommon coldFamily medicineUpper respiratory tract infectionRespiratory tract infectionsPediatricsUpper respiratory infectionsRespiratory systemPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Over-the-counter cold remedies are widely used for symptomatic relief of upper respiratory tract infections. The safety of these drugs is not well established in infants and their efficacy is questionable. Our aim was to study the attitude of family physicians and pediatricians toward the use of cold remedies in infants and children. A questionnaire was sent to 400 family physicians and 100 pediatricians randomly selected across Ontario. The overall response rate was 53.2%. Sixteen percent of family physicians recommended cold remedies for infants 0 to 6 months of age compared to 4% of the pediatricians (P = 0.01). For infants 6 to 12 months of age, the difference between pediatricians and family physicians persisted (14% and 38% of, respectively; P < 0.001). Despite that cold remedies are not proven to be effective and some safety issues are associated with their use in the pediatric age group, physicians still recommend them. Continuing medical education programs should address the issue.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.390
Teacher spread0.332 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2006
Admission routes2
Has abstractyes

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