Loss to follow-up of patients with malignant lymphoma
Bibliographic record
Abstract
Loss to follow-up (LTFU) in cancer patients is a serious problem, yet there is little data on this and on the underlying reasons. Of 144 paediatric and 431 adult patients with lymphoma diagnosed in 1997/1998 at King Faisal Specialist Hospital and Research Center, Riyadh (KFSHRC), 30% and 48.5%, respectively, were LTFU after 4 years (excluding patients known to have died). In 2001-2002, 196 paediatric and adult lymphoma patients at KFSHRC were enrolled in a prospective study in which explanations were obtained in detail for non-attendance at follow-up appointments (No Show). Sixteen months after commencement of the study, 49 patients were No Show, because of patient-based communication problems (20), transportation problems (8), patient not contactable (18), and personal reasons (3). In addition, patients were recorded incorrectly as No Show through hospital/patient communication problems. The No Show patients, especially the 23 who failed to keep a second appointment, are identifiable as potential LTFU during the 3 years in which this cohort will be followed. This study and, we suggest, other studies on LTFU should stimulate interest in this issue, in the predisposing factors, and in strategies to address them.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".