63. A Pain in the Neck: The Association Between Chronic Health Conditions and Frequent Consultation in Primary Care
Bibliographic record
Abstract
Background: A significant proportion of primary care consultations are taken up by a minority of patients (i.e. frequent consulters). Gender, age, socioeconomic status and mental health problems are associated with frequent consultation but there has been comparatively little work done examining the link with other common chronic conditions. The aim of this study was to examine the association between 10 common chronic conditions and frequent consultation in primary care. Methods: A prospective cohort study of adults aged 50 years and over registered with six GP practices in North Staffordshire (n = 6489) was conducted. The number of consultations during a six month period was obtained from medical record data. Frequent consultation was defined as the top 10%. The 10 chronic conditions were identified in a previous latent class analysis as being chronic, progressive and having significant impact on patients and healthcare use. These were hypertension (HTN), OA, diabetes (DM), hypercholesterolaemia, ischaemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), cervical spondylosis, atrial fibrillation (AF), hypothyroidism and congestive cardiac failure (CCF). For each condition the prevalence of frequent consultation was identified. Logistic regression estimated the association between frequent consultation and each chronic condition. This was adjusted for confounders [age, gender and comorbidity (an additional 2 or more chronic conditions)]. Results were expressed as an odds ratio (OR) with 95% CI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".