Changing Patterns and Predictability of Riskfactors Associated with BPD and Adverse Neurodevelopmental Outcome
Bibliographic record
Abstract
BACkgROuND: Non-attendance at outpatient clinics is an important obstacle to providing effective and efficient health care.Adolescents are widely reported as "poor attenders".Telephone or text-message reminders have been shown to significantly reduce the rate of missed appointments in different medical settings.OBjECTIVES: To evaluate the effect of appointment reminders sent as text-messages to patients' mobile phones on the rate of attendance at outpatient clinics.METhODS: This randomised trial was conducted at the youth clinic of a University Hospital between November 2010 and April 2011.Patients registered for an appointment at the clinic, and who gave a mobile phone number, were randomly selected to receive a reminder or not before the planned appointment.Patients were eligible each time they had an appointment.The outcome of interest was the rate of unexplained missed appointments.Appointments that were cancelled or re-scheduled before the planned appointments were not considered as missed.We considered a 10% improvement in the rate of missed appointment as a clinical relevant aim and powered the study accordingly.RESulTS: 991 patients were included (462 in the text-message group and 529 in the control group).The rate of missed appointments was 17.7% (95%CI: 13.1-19.8%) in the text-message group and 20.0% (95%CI: 16.6-23.4%)in the control group, showing no significant effect of the intervention (p=0.346).The rate of missed appointments differed slightly between the different types of consultations inside the clinic: 17.0% with textmessage vs 19.0% in the control group (p=0.614) in the general consultation and 13.9% with text-message vs 20.9% (p=0.266) in the control group in the gynecologic consultation.CONCluSION: In our primary care youth clinic, where most of the young patients are referred by school, social services, paediatricians or family doctors, text-message reminders are not effective in reducing significantly the proportion of missed appointments.Text-messaging may be effective in reducing missed appointments in our adolescent gynaecology clinic but further research is needed to confirm this.
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".