How essential are in‐person clinic visits during maintenance treatment of children with acute lymphoblastic leukemia?
Bibliographic record
Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Treatment consists of an initial intensive phase of chemotherapy, followed by a prolonged period of maintenance chemotherapy intended to reduce the risk of relapse. During the COVID-19 pandemic, the need arose to identify and reduce non-essential hospital visits. OBJECTIVE: We aimed to determine which proportion of in-person clinic visits during ALL maintenance therapy was associated with a change of management based on the results of the physical examination. PATIENTS AND METHODS: Medical records of children receiving maintenance chemotherapy for B-precursor ALL between September 2019 and February 2020 were reviewed. Visits with a new finding on physical examination were divided into those where an in-person assessment was deemed essential versus not essential. Finally, we determined the proportion of essential in-person visits that resulted in a change of management. RESULTS: A total of 240 maintenance visits by 75 children were analyzed. An abnormal finding on physical examination was noted during 20 visits (8.3%). Of those, 14 (5.8%) uncovered a new finding, six (2.5%) were classified as "in-person visit essential," and among those six visits, three (1.2%) resulted in a change of patient management (one for acute otitis media, one for wheezing, and one for limp). CONCLUSION: Our findings support the evaluation of care delivery models other than in-person visits during ALL maintenance therapy. A prospective study is required to delineate criteria, benefits/risks, and families' perspectives associated with virtual care delivery and the optimal frequency of in-person visits.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".