Exploring the use of decision support tools to evaluate cancer predisposition syndromes in pediatrics
Bibliographic record
Abstract
Background: Cancer predispositions syndromes (CPSs) are genetic conditions that increase the likelihood of developing cancer throughout a patient’s lifetime. For pediatric cancer patients, CPSs are particularly relevant, as this population is less likely to develop malignancies from environmental exposures or other cancer-associated lifestyle factors. In fact, recent advances in the field of cancer genetics have elucidated the importance of recognizing the multitude of CPSs that may impact treatment plans, cancer surveillance and/or preventative measures for pediatric patients and their families. As a result, the last 20 years have seen a rise in decision-support tools (DSTs) that aim to guide health care practitioners in their evaluations of underlying CPSs. Currently, the scope of DSTs used to evaluate pediatric CPSs has yet to be described and their clinical application across Canadian institutions is not well understood. Objectives: The primary goal of this thesis is to identify, describe and categorize the features of DSTs developed for the pediatric oncology population. The second goal is to establish how these tools are being adopted in clinical settings, by assessing their utility to pediatric hematologist- oncologists (PHOs) across Canadian tertiary-care hospitals. Methods: An initial scoping review was performed to identify the pediatric-adapted DSTs that utilize the patient’s clinical features to determine whether they are likely to have an underlying CPS. Using the Joanna Briggs Institute scoping review methodology, a systematic search strategy was developed and customized for MEDLINE and EMBASE databases. Subsequently, the tools identified in the scoping review informed a survey electronically distributed to PHOs across the 16 largest pediatric oncology departments in Canada. Their awareness and attitude towards DSTs were solicited on an anonymous basis. Results: Fourteen DSTs were identified, of which (8/14) (57%) have been internally or externally validated for clinical use. Half of the DSTs were specific to one CPS (7/14); the majority were published in a paper-based format (11/14); developed to input the patient’s tumour type (14/14), family history of cancer (12/14), non-malignant physical findings (8/14); and developed to output their recommendation in a dichotomous form (10/14).With the online survey, a total of 36 responses from PHOs were recorded: 18/36 (50%) of the respondents had previously used a DST, while 15/36 (41.7%) had not, and 3 were uncertain. Users of DSTs did not solely rely on the tool’s recommendation but used it as part of their decision-making process. Non-DST users were often unaware of the existence of these tools or how to gain access to them. Both DST users and non-users stated that a tool’s ease-of-use, its accessibility, and its promotion by their academic institution constitute the most important features for a tool’s adoption into their clinical practice.Conclusion: Fourteen pediatric CPS DSTs were identified through a scoping review; these were developed with a wide range of input/output parameters, formats, and types of CPSs and malignancies being evaluated. Despite the need for additional resources, the use of DSTs in clinical settings is not prominent, as half of the surveyed physicians have not previously used a DST and most tools remain unknown to them. With further development of DSTs’ ease-of-use, accessibility, and evidence of their clinical benefit, adoption of DSTs in clinical practices across the country may become more systematic and lead to the increased recognition of CPSs in pediatric patients
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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 | 0.050 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".