Failure to attend appointments and loss to follow-up: a prospective study of patients with malignant lymphoma in Riyadh, Saudi Arabia
Bibliographic record
Abstract
Failure to attend medical appointments (No Show) and loss to follow-up (LTFU) among patients with cancer can adversely affect their treatment and eventual outcome. In a 3-year prospective study of 199 patients with malignant lymphoma, all of those with No Shows were contacted, and reasons given for No Shows were categorized. Of the 340 No Shows, 34.1% were due to hospital-based communication problems, 17.6% to errors in patient communication with the hospital, 7.4% to transportation problems and 16.5% to other personal reasons. Almost one quarter (24.4%) of the patients were not contactable. Reasons for No Show in all categories were instructive as to patients' attitudes to treatment. Nineteen (12.2%) of the 156 patients who had not died in the 3-year follow-up period were identified as LTFU. These 19 LTFU patients accounted for 77 (22.6%) of all No Shows. The data indicate that LTFU in this cohort is significantly less frequent than in a prior cohort followed up for 3 years from 1997 to 1998. These findings suggest that some causes of No Show can be addressed, and individuals are identified as at particular risk for No Show and ultimately LTFU. This study points out that pre-emptive strategies to reduce No Shows may be feasible and efficacious.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".