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Record W4256582461 · doi:10.1111/bjd.20280

PA04 (P16): An assessment of the experiences of general practitioners and general practitioner trainees of referrals to paediatric dermatology services

2021· article· en· W4256582461 on OpenAlexaff
N.-Y Kang, Jennifer Harrington, Pekka Kannus, Elena Pope, Irene Lara‐Corrales

Bibliographic record

VenueBritish Journal of Dermatology · 2021
Typearticle
Languageen
FieldMedicine
TopicMedicine and Dermatology Studies History
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineFamily medicineDermatologyMedical education

Abstract

fetched live from OpenAlex

N. Fagan,1 F. Browne,2 Á. Carroll2 and A.D. Irvine3 1St. James's Hospital; 2National Rehabilitation Hospital; and 3Children’s Health Ireland at Crumlin, Dublin, Ireland General practitioners (GPs) are the gatekeepers to secondary care. The UK’s All-Party Parliamentary Group on Skin report acknowledges the challenges that many GPs have with diagnosing skin disease, resulting in inappropriate referrals. In Ireland, the system is overburdened and with waiting lists upwards of 3 years for a routine appointment, delays can be experienced by those requiring specialist care. This study aimed to investigate GP and GP trainees’ experiences of referring to paediatric dermatology services, how comfortable they were with diagnosing and treating skin disease, and to explore potential interventions. A 24-item online questionnaire was distributed to participants across Ireland using a GP forum and a database of GP referrers supplied by a major national secondary care centre. Trainees were invited through their training networks. Responses were received from 162 GPs and 44 trainees (n = 206). Fewer than half of respondents had a special interest in dermatology (47%) or a postgraduate qualification (34%). The majority (54%) estimated that dermatology cases made up 10–20% of their workload. Most referred less than once a month (63%) and most frequently at the fourth or fifth visit (47%). Unsurprisingly > 95% of respondents were comfortable diagnosing and treating common skin conditions such as eczema and acne. Interestingly, many were uncomfortable diagnosing common presentations such as melanocytic naevi (68%), drug eruptions (46%), birthmarks (35%) and infantile haemangiomas (32%). Some felt uncomfortable treating common conditions such as drug eruptions (37%), psoriasis (29%) and infected eczema (19%). The most frequent reasons for referral were severe skin disease (74%), uncertain diagnosis (70%) and parental request (44%). Nearly all (95%) believed integrated care pathways would be beneficial. Most wanted more education (92%) and felt they did not receive enough training (79%). Many respondents (43%) felt they did not have access to specific paediatric dermatology resources and would use national guidelines (88%). Over half (67%) were interested in managing chronic skin disease in children. Nonmandatory open-ended questions revealed that access to teledermatology was the most desired resource (n = 22), followed by national guidelines and specialist nurses. As expected, long waiting lists was the dominating complaint. As key stakeholders it is essential that GPs feel confident managing skin disease. The majority felt they would benefit from targeted interventions. Access to teledermatology services was the most commonly requested resource. Education on common conditions would be well received and could reduce the numbers of patients referred to secondary care, increasing capacity for complex cases.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.018
GPT teacher head0.327
Teacher spread0.309 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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