PERCEPTIONS OF MOLECULAR BIOMARKER TESTING IN CANCER DIAGNOSIS AMONG ONCOLOGY CLINICIANS
Bibliographic record
Abstract
Background: Molecular biomarker testing has become a cornerstone of precision oncology, guiding targeted therapy and improving diagnostic accuracy. However, its integration into routine cancer care, particularly in low- and middle-income countries, remains suboptimal due to multifactorial challenges. Objective: To explore the experiences, attitudes, and perceived barriers faced by oncology clinicians in implementing molecular biomarker testing in routine cancer care in Lahore, Pakistan. Methods: A qualitative descriptive study was conducted over eight months in Lahore, utilizing purposive sampling to recruit 23 oncology clinicians from public and private tertiary care hospitals. Semi-structured, in-depth interviews were audio-recorded, transcribed, and analyzed using Braun and Clarke’s thematic analysis framework. Data saturation was achieved and NVivo software supported systematic coding. Results: Five major themes emerged: clinical relevance and awareness, operational and logistical challenges, educational and training gaps, ethical and emotional dilemmas, and systemic and policy-driven barriers. Clinicians reported variability in biomarker knowledge, difficulties in interpreting results, infrastructure limitations, and lack of standardized protocols. Emotional strain in discussing ambiguous or unactionable results, and disparities in patient access due to financial constraints, were also prominent. Participants emphasized the need for clearer guidelines, institutional support, and continued medical education. Conclusion: The study highlights the complex landscape surrounding biomarker testing adoption in oncology practice within a resource-limited setting. Addressing the identified barriers through systemic reform, clinician support, and targeted education can enhance the practical uptake of precision diagnostics, ultimately improving cancer care delivery in similar contexts.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".