Treatment initiation and completion among head and neck squamous cell carcinoma patients in Tanzania
Bibliographic record
Abstract
OBJECTIVE: Few studies characterizing clinical outcomes of head and neck cancer (HNC) patients in sub-Saharan Africa report the proportion of patients who initiate and complete treatment, information integral to contextualizing survival outcomes. This retrospective cohort study describes HNC patients who presented to Muhimbili National Hospital and Ocean Road Cancer Institute in 2018, the highest-volume oncology tertiary referral centers in Tanzania. Logistic regression was applied to assess predictors of treatment initiation and completion. RESULTS: Among the 176 head and neck squamous cell carcinoma (HNSCC) patients, 34% (59) had no treatment documented, 34%(59) had documentation of treatment initiation but not completion, and 33%(58) had documentation of treatment completion based on the modalities started. Univariate logistic regression showed that late-stage disease was associated with increased odds of initiating treatment (OR 8.24, 95% CI 2.05-33.11, p = 0.003) and trends toward completing treatment (OR 7.41, 95% CI 0.90-60.99, p = 0.063). At last visit, 36.9%(65) were alive with a median follow up of 5.6 months (IQR 1.64-12.5 months). A large proportion of HNC patients who presented to MNH and ORCI did not initiate or complete treatment. These metrics are critical to contextualize care outcomes of HNC patients in resource-constrained health systems and develop interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".