The Supportive Care Needs of Individuals Living With Advanced or Metastatic Lung Cancer Receiving Targeted or Immunotherapies
Bibliographic record
Abstract
OBJECTIVE: Lung cancer is associated with the highest incidence and mortality of all cancers. New treatments, called targeted therapies (TT) and immunotherapies (IO), offer higher treatment efficacy and fewer side effects compared to traditional treatments but are accompanied by uncertainty and an unpredictable treatment course. There is a paucity of research on the experiences of individuals living with advanced or metastatic lung cancer receiving TT/IO, and even less is known about the supportive care needs of this population. METHODS: Twenty four participants from across Canada participated in semi-structured interviews regarding their supportive care needs. Thematic analysis was utilized to identify their supportive care needs. RESULTS: Qualitative coding identified unmet needs and challenges. All participants indicated difficulties with unmet supportive care needs, including psychological, informational, and practical needs. CONCLUSIONS: The exploration of supportive care experiences of patients receiving TT/IO exposes high distress and unmet needs. Results indicate the need for timely and accessible supportive cancer care. Results can inform patient advocacy efforts and the development of new services.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".