Proactive versus reactive feeding tube (FT) placement in head and neck cancer patients (HNC pts) receiving chemoradiation (CRT).
Bibliographic record
Abstract
11 Background: Most HNC pts treated with CRT develop severe treatment-related oropharyngeal mucositis. For pts with adequate nutritional status and swallowing function prior to CRT the optimal approach to nutritional management remains undefined. A subset of pts will require FTs to complete treatment, but at least 50% of pts with prophylactic FTs never use them (Madhoun 2011). At our center we have used a "reactive" approach which minimizes exposure to unnecessary FT placement. However, some pts suffer nutritional crises resulting in hospitalization, treatment interruption, and higher risks of complications from FT placement. We investigated the impact of a more proactive approach to FT placement in our pts. Methods: HNC pts treated with CRT who received a FT during treatment 2014-2018 were retrospectively identified from electronic medical records and clinical data extracted. "Proactive" FTs were not considered medically necessary but were placed within 15 days of starting CRT. Primary outcomes of interest were weight loss during CRT (WTL), FT complications, length of hospital stay and FT duration. Uni- and multivariate analyses were done and p values < 0.05 considered of interest. Results: 124 pts were identified: 87% male, median age 62.5 yrs (39-80) and 67% oropharyngeal primary. All received platinum-based chemotherapy or cetuximab (8 pts). 19 FTs were considered medically necessary, 94 were placed reactively and 11 placed proactively; 91% were percutaneous and 94% were used. Median weight was 79.0 kg (27.6-165.7) prior to CRT and 71.2 kg (26.8-148.2) after. Median WTL was 9.9% (-13.9 to 33.3%). Proactive FT placement was associated with less WTL (p = 0.045). WTL was greater in pts over age 65 (OR 11.6 [1.4-97.6]). Hospital stay was longer for pts who had reactive FT. Females had longer FT duration (p = 0.005). Conclusions: These data suggest that when a reactive approach to FT placement is used, earlier FT placement during CRT is more effective at reducing weight loss and hospitalization time in this population. Specific criteria for pt selection with this approach should be defined. Further research to define the optimal approach to nutritional management in HNC pts receiving CRT is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".