Oncologists’ Use of Genomic Sequencing Data to Inform Clinical Management
Bibliographic record
Abstract
PURPOSE: To determine whether oncologists intended to change treatment as a result of tumor sequencing, and subsequently, whether patients experienced an alteration of clinical management or derived clinical benefit. PATIENTS AND METHODS: A prospective survey of oncologists referring adult patients with rare, advanced, or refractory cancer to the Michigan Oncology Sequencing program was conducted from June 2014 to March 2015 to assess the use of and intent to disclose sequencing findings. Oncologists' responses were compared with the referred patients' self-reported survey responses, and a content analysis of disclosure documented in the medical record was performed. Medical records were reviewed retrospectively to determine if clinical management was informed or changed by sequencing results. RESULTS: Oncologists (response rate, 93%) referring 112 consecutive patients were surveyed. Medical records of patients were reviewed for changes in clinical management on the basis of sequencing findings. Oncologists intended to change the treatment of 22% of patients (n = 24) on the basis of sequencing findings. Of these patients, 37.5% (n = 9) had an actual change in clinical management. Thirty-four patients with postsequencing survey data reported that a results disclosure discussion did not occur, despite documentation of disclosure by the physician in the medical record. CONCLUSIONS: Findings demonstrate that many oncologists view next-generation sequencing results to be potentially valuable in directing subsequent therapy for their patients; however, barriers in communicating results to patients and implementing them in clinical management remain.
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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".