Hemoglobin Monitoring in Head and Neck Cancer Patients Undergoing Radiotherapy
Bibliographic record
Abstract
OBJECTIVES: Anemia is a well-recognized factor for local recurrence and decreased survival in cancer patients undergoing radiotherapy. Additionally, lower hemoglobin (Hb) levels have a negative impact on radiotherapy efficacy and response rates. The objective of this audit was to investigate how frequently Hb levels were observed in head and neck cancer patients receiving radiotherapy within a multidisciplinary team setting. METHODS: We performed a retrospective first-cycle audit in a university hospital in Glasgow that is a tertiary referral center for head and neck cancer. Included were 78 patients with head and neck cancer who were undergoing radiotherapy. Online laboratory services and clinical case sheets were checked for each patient to monitor the frequency of observation of Hb levels before, during, and after radiotherapy. RESULTS: Of these 78 patients, only 49 had their Hb level checked before radiotherapy treatment, only 9 during radiotherapy, and only 27 after completion of radiotherapy treatment (p < 0.0001). Of the 49 patients with preradiotherapy Hb levels available, 24% were found to be anemic; none of these patients had their Hb monitored during radiotherapy, and only 4 had Hb levels recorded after completion of treatment. CONCLUSIONS: This audit has highlighted that despite evidence emphasizing that anemia in cancer is an independent prognostic factor for recurrence, there is no formal protocol for Hb monitoring in head and neck cancer patients undergoing radiotherapy. The audit has also demonstrated that Hb monitoring is infrequently performed and that subsequent observation of the Hb level is suboptimal.
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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 | 0.005 |
| 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 | 0.000 |
| 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".