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Case ascertainment in active paediatric surveillance systems: a report from the British Paediatric Surveillance Unit Ascertainment Group

2019· article· en· W2954216432 on OpenAlexaff
Richard Lynn, Richard Reading

Bibliographic record

VenueArchives of Disease in Childhood · 2019
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsMedicineIncidence (geometry)PediatricsPublic healthUnit (ring theory)Public health surveillanceEnvironmental healthHealth surveillanceEpidemiologyFamily medicinePathology

Abstract

fetched live from OpenAlex

The British Paediatric Surveillance Unit (BPSU) conducts surveillance of rare paediatric conditions using active, or prospective, case finding. The reliability of estimates of incidence, which is the primary outcome of public health importance, depends on ascertainment being as near complete as possible. This paper reviews evidence of the completeness of ascertainment in recent surveillance studies run through the BPSU. Ascertainment varied between 49% and 94% depending on the study. These are upper estimates. This was the basis of a discussion on barriers and facilitators of ascertainment which we have separated into factors related to the condition, factors related to the study methods, factors related to the study team and factors related to the surveillance system infrastructure. This leads to a series of recommendations to ensure continuing high levels of ascertainment in active surveillance studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.294
Teacher spread0.279 · 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.

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

Citations7
Published2019
Admission routes1
Has abstractyes

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