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Provision of dental care for medically compromised children in the UK by General Dental Practitioners

2000· article· en· W2097315842 on OpenAlexafffund
Jennifer Parry, Fahmida Afroz Khan

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2000
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsSt. Thomas Hospital
FundersHospital for Sick Children
KeywordsMedicineDental careFamily medicineHaemophiliaPediatrics

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the views of General Dental Practitioners (GDPs) regarding their provision of dental treatment for medically compromised children. A questionnaire to assess confidence, experience and willingness to treat eight specific groups of medically compromised children was sent to 524 GDPs. Information is based on 271 completed questionnaires. The median number of children treated by GDPs in each of the eight groups over the previous 5 years was 0-2. Eighty percent of respondents stated that they would value further training in the provision of dental care for medically compromised children. Confidence was highest in providing dental treatment for children with three conditions: congenital heart disease (37% very confident), diabetes (39% very confident) and epilepsy (41% very confident). These were also the conditions that the GDPs reported as presenting most frequently in the dental surgery. GDPs reported least confidence in providing dental care for children with haemophilia (12% very confident) and organ transplants (14% very confident). Only 30% of GDPs wanted to be routinely involved with the provision of dental care for medically compromised children. The results indicate that medically compromised children may have problems accessing dental care and expertise. A system of integrated medical and specialised dental care is suggested.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.047
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.351
Teacher spread0.340 · 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 teacher head, 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

Citations31
Published2000
Admission routes2
Has abstractyes

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