To treat or not to treat? Oncologists’ perceptions and experiences regarding overtreatment in end stage cancer patients
Bibliographic record
Abstract
Introduction: Aggressive care at end-of-life can harm quality of life without significantly improving survival. Despite best practice guidelines, research shows that oncologists continue to provide too much treatment to patients, especially at the end-of-life. Understanding the perceptions of oncologists regarding unnecessary care toward end-of-life can inform interventions and mitigate overuse. This study aimed to understand the perceptions and experiences of oncologists regarding why overuse of services is occurring for cancer patients at the end-of-life and elucidate factors which impede the implementation of best practices at the end-of-life in cancer. Methods: In-depth, semi-structured interviews were conducted with oncologists in Israel. The interview guide was based on the Theoretical Domains Framework to identify beliefs about practices in caring for patients at the end-of-life and transitioning to palliative care. Interviews were audio-recorded, transcribed, coded, and thematically analyzed. Results: Participants identified six major barriers and 12 major facilitators to reducing overuse at end-of-life. Barriers included patients seeking second opinions, patient and family fragility, pressure and demands from patients and families, a culture of valuing extending life, time constructs, and physicians' emotional regulation. Physicians reduce overuse by relying on experience, communication and relationship building skills, taking ownership over their roles, confidence in their abilities, belief and recognition of the importance of appropriate care, involving families and other healthcare professionals and easing into the process. Oncologist opinions vary based on role and geographical area of practice. Conclusion: Physicians can influence the rate of overuse as they guide patients at end-of-life. Findings can be utilized to help the health system in Israel reduce the overuse of unnecessary services at the end-of-life for cancer. Interventions such as palliative care referrals, multidisciplinary teams, and educational initiatives can help minimize overuse and improve quality of life for patients in their final days. Future research should incorporate views and perspectives of other stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".