Qualitative Assessment of Pain Management in Patients Undergoing Computed Tomography‐Guided Transthoracic Lung Biopsy
Bibliographic record
Abstract
BACKGROUND: Unpublished questionnaire data collected by the authors' institution (Toronto General Hospital, Toronto, Ontario) indicates that patients often experience significant pain while undergoing lung biopsy, yet receive only a fraction of the maximum allowable dosage of analgesic. OBJECTIVES: To qualitatively assess current pain management practices from the patient perspective. Patient education and patient satisfaction were also evaluated. METHODS: From March through June 2012, participants were contacted via telephone within one week of their procedure until data saturation was reached. The semistructured interviews were based on a study-specific interview template. Thematic analysis of qualitative study data was used to identify recurring interview topics. RESULTS: A consecutive sample of 16 outpatients who had undergone image-guided transthoracic lung biopsy at the authors' institution were interviewed. None of the study participants reported noteworthy pain associated with the insertion of lung biopsy needles. The most significant pain was caused by positioning within the computed tomography scanner, particularly among participants who were in the prone position. All participants reported high satisfaction with the amount of analgesic received. Potential complications and recovery period details were identified as areas for improved patient education. CONCLUSIONS: At the authors' institution, pain associated with lung biopsy needle insertion was well controlled. Positional pain is common for patients required to be in the prone position. Potential solutions include increasing awareness of positional pain and instituting additional supportive equipment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".