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Record W76387059 · doi:10.1093/pch/12.1.15

Waiting times in a tertiary paediatric nephrology clinic

2007· article· en· W76387059 on OpenAlexaffabout
Guido Filler, Marilyn Sutandar, Darlene Poulin

Bibliographic record

VenuePaediatrics & Child Health · 2007
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineNephrologyReferralPediatricsInternal medicineIncidence (geometry)Tertiary referral hospitalFamily medicineEmergency medicineIntensive care medicineRetrospective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: To the authors' knowledge, paediatric nephrology waiting times have not been previously studied. Given the high incidence of new referrals each year, the evaluation of the current waiting times would be beneficial in the management and triaging of new appointments. PATIENTS AND METHODS: Using descriptive statistics, data from all appropriate paediatric nephrology referrals to the Children's Hospital of Eastern Ontario (Ottawa, Ontario) from 2003 to 2005 (n=1446) were retrospectively analyzed. RESULTS: The median waiting time from receipt of initial request for referral to first appointment was 111 days (range zero to 364 days). No significant variation existed throughout the duration of the study, despite the variation in the number of paediatric nephrology staff. Infants were seen significantly sooner than older children. There were no assigned priority classification levels based on referral reason. Critical conditions, such as macrohematuria, were seen on an urgent basis; all other patients were seen at the next available appointment slot, which was usually four months away. A significant proportion of patients were referred for dysfunctional voiding and enuresis (25.9%). These diagnoses are not generally considered a part of core nephrology. CONCLUSION: The waiting times for a paediatric nephrology appointment are long. Focusing on core nephrology business and appropriate triaging of consult would be necessary to implement a priority classification level-based appointment assignment. Additional resources would allow for more patients to be seen in a more timely fashion.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.388
Teacher spread0.355 · 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

Citations8
Published2007
Admission routes2
Has abstractyes

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