Predicting Patient Attendance in the Neuromuscular Clinic: A Logistic Regression Analysis (P7.336)
Bibliographic record
Abstract
OBJECTIVE: To identify factors which determine patient attendance at the neurophysiology laboratory in order to maximize efficient use of resources. BACKGROUND: The rate of patient attendance at clinical appointments is an important determinant of efficiency, for the individual clinic and for the health system as a whole. By modelling past patterns of attendance, it is hoped that we may predict with greater certainty the likelihood of individual patients making their appointments. With this information, particular efforts may be directed towards patients at high risk of missing their appointment. DESIGN/METHODS: We used logistic regression techniques to analyze a prospectively assembled cohort of patients referred to a tertiary neurophysiology laboratory in order to identify factors which might predict their probability of attending. Dependent variables included demographic factors such as age, gender, identity of consultant physician and specialty of the referring physician; temporal factors, such as the time and date of the appointment, or the day of the week; and environmental factors, such as temperature and precipitation. RESULTS: A total of n=910 clinical appointments were identified, of which 828 (91[percnt]) were attended. There was a significant association between patient attendance and patient age (P=0.0037), with younger patients less likely to attend their appointments. Follow up visits were also more likely to be attended (P=0.025). There was no significant association with consultant physician, patient gender, time of appointment, day of the week, or month of the year. CONCLUSIONS: This study shows that patient attendance patterns may be modeled effectively using logistic regression techniques. In our cohort, patient age was the most significant predictor of attendance, followed by follow up visits. These results suggest that efforts to remind patients about their appointments would be best directed towards younger patients and to patients new to the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".