Diagnostic and treatment challenges of adolescent and young adult oncology patients.
Bibliographic record
Abstract
9557 Background: Adolescent and young adult (AYA) cancer patients are faced with obstacles and challenges related to their diagnosis and treatment compared to children and older adults. The aim of this study was to explore the patient and health care system-related delays in the interval from cancer symptom onset to diagnosis and treatment as well as to identify the possible contributing factors to these delays in the AYA group. Methods: This study was based on a questionnaire conducted in 2010-2011 completed by patients diagnosed with a malignancy between the ages of 16 and 39 in addition to older patients diagnosed with a pediatric type malignancy. Four categories of delays: patient delay (time from patient symptom onset until first health care contact date), health care system delay (time from first health care contact until diagnosis date), treatment delay (time from diagnosis date until first treatment) and oncologist delay (time from first health care contact until first medical oncologist meeting) were calculated. Median delay (in days) with interquartile interval (IQI) was the main outcome measure. Median time for each category of delay was further analysed to explore how they vary with different patient characteristics. Results: We identified a median patient delay of 30 days (IQI 1-131), a median health care system delay of 53 days (IQI 1-213), a median treatment delay of 36 days (IQI 5-92) and a median oncologist delay of 77 days (IQI 30-281). Patient delay was affected by patient gender, age at diagnosis and type of first health care contact. Health care system delay was associated with patient marital status, financial situation and attitude of first health care professional. Treatment delay was related to type of cancer. Conclusions: The health care system delay (including oncologist delay) accounts for much of the delay from symptom onset to first treatment. Professional characteristics of frontline medical personnel as well as socioeconomic and biological characteristics of the patients may contribute to delay. Healthcare professionals and the general community as a whole need to be aware of the factors contributing to delay in diagnosis and treatment in the underserved patient population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".